Author Information

Pravish Sainath (Mila / University of Montreal)

The purpose of my life and study is to better understand the nature and mechanisms of both natural and artificial intelligence. My broad interests span the intersection between the two vast disciplines of artificial intelligence and neuroscience. I am mostly interested in using computational and cognitive neuroscience to improve deep and reinforcement learning methods and applying deep learning methods on neuroimaging data to better understand the brain. Previously, I was an engineer at the startup [Hammerhead](http://www.hammerhead.io/), where I worked on computing cycling fitness metrics from smart cycling sensors for their flagship product Karoo and developing navigation solutions for the platform.

Mohamed Akrout (University of Toronto)

Charles Delahunt (University of Washington Seattle)

Nathan Kutz (University of Washington)

Guangyu Robert Yang (New York University)

Joseph Marino (California Institute of Technology)

L F Abbott (Columbia University)

Nicolas Vecoven (University of Liège)

Damien Ernst (University of Liège)

andrew warrington (university of oxford / university of british columbia)

Michael Kagan (SLAC / Stanford)

Kyunghyun Cho (New York University)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Kameron Harris (University of Washington)

Leopold Grinberg (IBM)

John J. Hopfield (Princeton University)

BA Swarthmore 1954; PhD Cornell (theoretical physics) 1958. Member of technical staff Bell Laboratories 1958-1960 & 1973-1996; Faculty positions at UCBerkeley (physics) 1961-1964, Princeton Univ. (physics) 1964-1980, Caltech (chemistry and biology) 1980-1996, Princeton Univ. (molecular biology) 1997-2008, Institute for Advanced Study (2010-2013), now emeritus at Princeton Neuroscience Institute. Served as Chairman of the Faculty, Caltech; President of the American Physical Society; Executive Officer for Computation and Neural Systems, Caltech. Honors include Buckley Prize in Solid State Physics; APS prize in biophysics; Dirac Medal; Einstein Award; MacArthur Fellow; IEEE Rosenblatt Award; Swartz Prize in Computational Neuroscience. Member, National Academy of Science; American Philosophical Society. Research on the interaction of light with solids 1956-1970; biomolecular physics and kinetic proofreading 1970-1980; neural network dynamics and neurobiology 1980-.

Dmitry Krotov (IBM Research)

Taliah Muhammad (Baylor College of Medicine)

Erick Cobos (Baylor College of Medicine)

Edgar Walker (Baylor College of Medicine)

Jacob Reimer (Baylor College of Medicine)

Andreas Tolias (Baylor College of Medicine)

Alexander Ecker (University of Tuebingen)

Janaki Sheth (University of California at Los Angeles)

Yu Zhang (University de Montreal)

IVADO Postdoctoral Fellow working on applying deep learning tools to Neuroscience research Ph.D. in Pattern Recognition and Intelligent Systems * 10 years of experience in Neuroscience research, working with structural, functional and diffusion MRI * skilled coding with Matlab, R, and Python, familiar with the deep learning platform including Tensorflow, Keras and Pytorch * Currently working on the project of predicting human cognition and behaviors based on Neuroimage data and deep learning models You can check my publications here: https://scholar.google.ca/citations?user=lZwQ9mgAAAAJ&hl=en

Maciej Wołczyk (Jagiellonian University)

Jacek Tabor (Jagiellonian University)

Szymon Maszke (Uniwersytet Jagielloński)

Roman Pogodin (University College London)

Dane Corneil (benevolent.ai)

Wulfram Gerstner (EPFL)

Baihan Lin (Columbia University)

Guillermo Cecchi (IBM Research)

Jenna M Reinen (IBM Research)

Irina Rish (MILA / Université de Montréal)

Guillaume Bellec (Graz University of Technology)

Darjan Salaj (Graz University of Technology)

Anand Subramoney (Graz University of Technology)

Wolfgang Maass (Graz University of Technology)

Yueqi Wang (Columbia University)

Ari Pakman (Columbia University)

Jin Hyung Lee (Columbia University)

Liam Paninski (Columbia University)

Bryan Tripp (University of Waterloo)

Colin Graber (University of Illinois at Urbana-Champaign)

Alex Schwing (University of Illinois at Urbana-Champaign)

Luke Prince (University of Toronto)

Gabriel Ocker (Allen Institute for Brain Science)

Michael Buice (Allen Institute for Brain Science)

Benjamin Lansdell (University of Pennsylvania)

Konrad Kording (Upenn)

Jack Lindsey (Columbia University)

Terrence Sejnowski (Salk Institute)

Matthew Farrell (University of Washington)

Eric Shea-Brown (University of Washington)

Nicolas Farrugia (IMT Atlantique)

Nicolas Farrugia obtained his PhD in 2008, on hardware implementation of convolutional neural networks. In 2010, Nicolas Farrugia moved to the field of neurosciences with a focus on music. He uses a wide range of cognitive neuroscience methods such as EEG, functional MRI, as well as behavioral psychology methods and motion capture. In 2015, he joins Telecom Bretagne to engage into a transdisciplinary effort, combining methods from Neuroscience, Deep Learning and hardware implementations.

Victor Nepveu (IMT Atlantique)

Jiwoong Im (Janelia Research Center)

Kristin Branson (Janelia Research Campus, HHMI)

Brian Hu (Allen Institute for Brain Science)

Ramakrishnan Iyer (Allen Institute for Brain Science)

Stefan Mihalas (Allen Institute for Brain Science)

Sneha Aenugu (University of Massachussets Amherst)

Hananel Hazan (University of Massachusetts Amherst)

Sihui Dai (California Institute of Technology)

Tan Nguyen (Rice University)

I am currently a postdoctoral scholar in the Department of Mathematics at the University of California, Los Angeles, working with Dr. Stanley J. Osher. I have obtained my Ph.D. in Machine Learning from Rice University, where I was advised by Dr. Richard G. Baraniuk. My research is focused on the intersection of Deep Learning, Probabilistic Modeling, Optimization, and ODEs/PDEs. I gave an invited talk in the Deep Learning Theory Workshop at NeurIPS 2018 and organized the 1st Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. I also had two awesome long internships with Amazon AI and NVIDIA Research, during which he worked with Dr. Anima Anandkumar. I am the recipient of the prestigious Computing Innovation Postdoctoral Fellowship (CIFellows) from the Computing Research Association (CRA), the NSF Graduate Research Fellowship, and the IGERT Neuroengineering Traineeship. I received his MSEE and BSEE from Rice in May 2018 and May 2014, respectively.

Doris Tsao (Caltech)

Richard Baraniuk (Rice University)

Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Hidenori Tanaka (Stanford)

Aran Nayebi (Stanford University)

Stephen Baccus (Stanford University)

Surya Ganguli (Stanford)

Dean Pospisil (University of Washington)

Eilif Muller (Element AI)

Applied Research Scientist at Element AI. Looking to neuroscience for inspiration in AI, and vice versa, in particular when it comes to neocortical architectures for learning to represent the world around us. From 2011-2019, I led the team of researchers at the Blue Brain Project developing, and studying state-of-the-art simulations of neocortical brain tissue.

Jeffrey S Cheng (University of Pennsylvania)

I'm a ML consultant at Palantir. My research interests center around applying smart inductive biases to generative natural processes. I'm currently working on creating interpretable models for machine translation and music composition.

Gaël Varoquaux (INRIA)

Kamalaker Dadi (INRIA)

Dimitrios C Gklezakos (University of Washington)

Rajesh PN Rao (University of Washington)

Anand Louis (Indian Institute of Science, Bengaluru)

Christos Papadimitriou (Columbua U)

Santosh Vempala (Georgia Tech)

Naganand Yadati (Indian Institute of Science)

Daniel Zdeblick (University of Washington)

Daniela M Witten (University of Washington)

Nicholas Roberts (Carnegie Mellon University)

Vinay Prabhu (UnifyID Inc)

Pierre Bellec (Université de Montréal)

I am a principal investigator at the Unité de neuroimagerie fonctionnelle, Centre de recherche de l'institut de gériatrie de Montréal, “professeur adjoint sous octroi” with the computer science and operations research department (DIRO) and a member of the Institute of Biomedical Engineering at Université de Montréal. I am developing machine learning tools to study the brain structure and function using magnetic resonance imaging and I use these tools to explore the processes of brain reorganization in healthy aging and neurodegenerative diseases.

Poornima Ramesh (Technical University of Munich)

Jakob H Macke (Technical University of Munich, Munich, Germany)

Santiago Cadena (University of Tübingen)

Guillaume Bellec (TU Graz)

Franz Scherr (Graz University of Technology)

Owen Marschall (New York University)

BA in Mathematics and Physics (Amherst College) PhD student in Neural Science (New York University)

Robert Kim (Salk Institute)

Hannes Rapp (University of Cologne)

Marcio Fonseca (Câmara dos Deputados)

ML Engineer @ Camara dos Deputados. MSc Cognitive Science @ The University of Edinburgh.

Oliver Armitage (BIOS)

Jiwoong Im (Janelia Research Center)

Thomas Hardcastle (BIOS.health)

Abhishek Sharma (University of Massachusetts, Amherst)

Wyeth Bair (University of Washington)

Adrian Valente (ENS)

Shane Shang (University of Washington)

Merav Stern (University of Washington)

Rutuja Patil (Janelia Research Campus)

Peter Wang (Columbia University)

Sruthi Gorantla (National University of Singapore)

Peter Stratton (The University of Queensland)

Tristan Edwards (BIOS Health)

Jialin Lu (Simon Fraser University)

MSc student at Simon Fraser University with Prof. Martin Ester. I am generally interested in interpretability: interpret a complex prediction system into a small and interpretable structure. Recently I got especially interested in integrating symbolic methods with scalable deep learning approaches.

Martin Ester (Simon Fraser University)

Martin Ester received a PhD in Computer Science from ETH Zurich, Switzerland, in 1989. He has been working for Swissair developing expert systems before he joined University of Munich as an Assistant Professor in 1993. Since November 2001, he has first been an Associate Professor and now a Full Professor at the School of Computing Science of Simon Fraser University. From May 2010 to April 2015, he has served as the School Director. Dr. Ester has published extensively in the top conferences and journals of his field such as ACM SIGKDD, WWW, ACM RecSys, ISMB, and PSB. According to Google Scholar, his publications have received more than 53'000 citations, and his h-index is 70. He received the KDD 2014 Test of Time Award for his paper on DBSCAN, was elected as a Fellow of the Royal Society of Canada in 2019, and was appointed Distinguished Professor at Simon Fraser University in 2021. Martin Ester’s research interests are in the area of data mining and machine learning, with a current focus on transfer learning, causal discovery and inference, explainable machine learning, and clustering. Many of the driving applications of his research are in the biomedical field, and he has an honorary appointment as Senior Research Scientist at the Vancouver Prostate Center.

Yurii Vlasov (University of Illinois at Urbana Champaign)

Siavash Golkar (Flatiron Institute)

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  Tam Nguyen · Tan Nguyen · Richard Baraniuk
• 2023 Poster: Energy Guided Diffusion for Generating Neurally Exciting Images »
  Pawel Pierzchlewicz · Konstantin Willeke · Arne Nix · Pavithra Elumalai · Kelli Restivo · Tori Shinn · Cate Nealley · Gabrielle Rodriguez · Saumil Patel · Katrin Franke · Andreas Tolias · Fabian Sinz
• 2023 Poster: Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data »
  Praveen Venkatesh · Corbett Bennett · Sam Gale · Tamina Ramirez · Greggory Heller · Severine Durand · Shawn Olsen · Stefan Mihalas
• 2023 Poster: Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials »
  Shengchao Liu · weitao Du · Yanjing Li · Zhuoxinran Li · Zhiling Zheng · Chenru Duan · Zhi-Ming Ma · Omar Yaghi · Animashree Anandkumar · Christian Borgs · Jennifer Chayes · Hongyu Guo · Jian Tang
• 2023 Poster: IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL »
  Pascal Leroy · Pablo G. Morato · Jonathan Pisane · Athanasios Kolios · Damien Ernst
• 2023 Poster: LeanDojo: Theorem Proving with Retrieval-Augmented Language Models »
  Kaiyu Yang · Aidan Swope · Alex Gu · Rahul Chalamala · Peiyang Song · Shixing Yu · Saad Godil · Ryan J Prenger · Animashree Anandkumar
• 2023 Poster: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate models »
  Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Julius Busecke · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Tian Zheng · Mike Pritchard
• 2023 Oral: LeanDojo: Theorem Proving with Retrieval-Augmented Language Models »
  Kaiyu Yang · Aidan Swope · Alex Gu · Rahul Chalamala · Peiyang Song · Shixing Yu · Saad Godil · Ryan J Prenger · Animashree Anandkumar
• 2023 Oral: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate models »
  Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Julius Busecke · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Tian Zheng · Mike Pritchard
• 2023 Workshop: Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization »
  Julia Gusak · Jean Kossaifi · Alena Shilova · Cristiana Bentes · Animashree Anandkumar · Olivier Beaumont
• 2023 Workshop: Learning-Based Solutions for Inverse Problems »
  Shirin Jalali · christopher metzler · Ajil Jalal · Jon Tamir · Reinhard Heckel · Paul Hand · Arian Maleki · Richard Baraniuk
• 2023 Workshop: The Symbiosis of Deep Learning and Differential Equations -- III »
  Luca Celotti · Martin Magill · Ermal Rrapaj · Winnie Xu · Qiyao Wei · Archis Joglekar · Michael Poli · Animashree Anandkumar
• 2023 Workshop: New Frontiers of AI for Drug Discovery and Development »
  Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Wei Wang
• 2022 : Contributed Talk: DensePure: Understanding Diffusion Models towards Adversarial Robustness »
  Zhongzhu Chen · Kun Jin · Jiongxiao Wang · Weili Nie · Mingyan Liu · Anima Anandkumar · Bo Li · Dawn Song
• 2022 : A Universal Abstraction for Hierarchical Hopfield Networks »
  Benjamin Hoover · Duen Horng Chau · Hendrik Strobelt · Dmitry Krotov
• 2022 Workshop: Trustworthy and Socially Responsible Machine Learning »
  Huan Zhang · Linyi Li · Chaowei Xiao · J. Zico Kolter · Anima Anandkumar · Bo Li
• 2022 : Lessons Learned »
  Konstantin Willeke · Paul Fahey · Andreas Tolias · Fabian Sinz
• 2022 : Conclusion, Discussion, Outlook »
  Konstantin Willeke · Paul Fahey · Andreas Tolias · Fabian Sinz
• 2022 : Invited talk: "How good would your model be if you had an infinite amount of noiseless data?" »
  Dean Pospisil
• 2022 Competition: The SENSORIUM competition on predicting large scale mouse primary visual cortex activity »
  Konstantin Willeke · Paul Fahey · Mohammad Bashiri · Laura Hansel · Max Burg · Christoph Blessing · Santiago Cadena · Zhiwei Ding · Konstantin-Klemens Lurz · Kayla Ponder · Subash Prakash · Kishan Naik · Kantharaju Narayanappa · Alexander Ecker · Andreas Tolias · Fabian Sinz
• 2022 : Panel »
  Vinay Prabhu · Lamtharn (Hanoi) Hantrakul · Stella Biderman · Deven Desai
• 2022 : EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation »
  Jae Hyeon Lee · Payman Yadollahpour · Andrew Watkins · Nathan Frey · Andrew Leaver-Fay · Stephen Ra · Vladimir Gligorijevic · Kyunghyun Cho · Aviv Regev · Richard Bonneau
• 2022 : HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression »
  Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan
• 2022 : Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models »
  Zhuoran Qiao · Weili Nie · Arash Vahdat · Thomas Miller · Anima Anandkumar
• 2022 Workshop: Machine Learning and the Physical Sciences »
  Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová · Rianne van den Berg
• 2022 : Recall-gated plasticity as a principle of systems memory consolidation »
  Jack Lindsey · Ashok Litwin-Kumar
• 2022 Workshop: Robustness in Sequence Modeling »
  Nathan Ng · Haoran Zhang · Vinith Suriyakumar · Chantal Shaib · Kyunghyun Cho · Yixuan Li · Alice Oh · Marzyeh Ghassemi
• 2022 Workshop: AI for Science: Progress and Promises »
  Yi Ding · Yuanqi Du · Tianfan Fu · Hanchen Wang · Anima Anandkumar · Yoshua Bengio · Anthony Gitter · Carla Gomes · Aviv Regev · Max Welling · Marinka Zitnik
• 2022 Poster: SIXO: Smoothing Inference with Twisted Objectives »
  Dieterich Lawson · Allan Raventós · andrew warrington · Scott Linderman
• 2022 Poster: Generative multitask learning mitigates target-causing confounding »
  Taro Makino · Krzysztof Geras · Kyunghyun Cho
• 2022 Poster: Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space »
  Yunbum Kook · Yin-Tat Lee · Ruoqi Shen · Santosh Vempala
• 2022 Poster: Action-modulated midbrain dopamine activity arises from distributed control policies »
  Jack Lindsey · Ashok Litwin-Kumar
• 2022 Poster: Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators »
  Yuhan Helena Liu · Stephen Smith · Stefan Mihalas · Eric Shea-Brown · Uygar Sümbül
• 2022 Poster: Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models »
  Manli Shu · Weili Nie · De-An Huang · Zhiding Yu · Tom Goldstein · Anima Anandkumar · Chaowei Xiao
• 2022 Poster: Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks »
  Mansheej Paul · Brett Larsen · Surya Ganguli · Jonathan Frankle · Gintare Karolina Dziugaite
• 2022 Poster: PeRFception: Perception using Radiance Fields »
  Yoonwoo Jeong · Seungjoo Shin · Junha Lee · Chris Choy · Anima Anandkumar · Minsu Cho · Jaesik Park
• 2022 Poster: Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules »
  Yuhan Helena Liu · Arna Ghosh · Blake Richards · Eric Shea-Brown · Guillaume Lajoie
• 2022 Poster: CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations »
  Kai Yan · Alex Schwing · Yu-Xiong Wang
• 2022 Poster: Beyond neural scaling laws: beating power law scaling via data pruning »
  Ben Sorscher · Robert Geirhos · Shashank Shekhar · Surya Ganguli · Ari Morcos
• 2022 Poster: Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits »
  Tianyuan Jin · Pan Xu · Xiaokui Xiao · Anima Anandkumar
• 2022 Poster: DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data »
  Tiantian Fang · Ruoyu Sun · Alex Schwing
• 2022 Poster: Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy »
  Siavash Golkar · Tiberiu Tesileanu · Yanis Bahroun · Anirvan Sengupta · Dmitri Chklovskii
• 2022 Poster: Learning Chaotic Dynamics in Dissipative Systems »
  Zongyi Li · Miguel Liu-Schiaffini · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Kaushik Bhattacharya · Andrew Stuart · Anima Anandkumar
• 2022 Poster: Learning dynamics of deep linear networks with multiple pathways »
  Jianghong Shi · Eric Shea-Brown · Michael Buice
• 2022 Poster: Mesoscopic modeling of hidden spiking neurons »
  Shuqi Wang · Valentin Schmutz · Guillaume Bellec · Wulfram Gerstner
• 2022 Poster: Kernel Memory Networks: A Unifying Framework for Memory Modeling »
  Georgios Iatropoulos · Johanni Brea · Wulfram Gerstner
• 2022 Poster: Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models »
  Boxin Wang · Wei Ping · Chaowei Xiao · Peng Xu · Mostofa Patwary · Mohammad Shoeybi · Bo Li · Anima Anandkumar · Bryan Catanzaro
• 2022 Poster: Pre-Trained Language Models for Interactive Decision-Making »
  Shuang Li · Xavier Puig · Chris Paxton · Yilun Du · Clinton Wang · Linxi Fan · Tao Chen · De-An Huang · Ekin Akyürek · Anima Anandkumar · Jacob Andreas · Igor Mordatch · Antonio Torralba · Yuke Zhu
• 2022 Poster: Biological Learning of Irreducible Representations of Commuting Transformations »
  Alexander Genkin · David Lipshutz · Siavash Golkar · Tiberiu Tesileanu · Dmitri Chklovskii
• 2022 Poster: Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks »
  Renan A. Rojas-Gomez · Teck-Yian Lim · Alex Schwing · Minh Do · Raymond A. Yeh
• 2022 Poster: MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge »
  Linxi Fan · Guanzhi Wang · Yunfan Jiang · Ajay Mandlekar · Yuncong Yang · Haoyi Zhu · Andrew Tang · De-An Huang · Yuke Zhu · Anima Anandkumar
• 2022 Poster: S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces »
  Eric Nguyen · Karan Goel · Albert Gu · Gordon Downs · Preey Shah · Tri Dao · Stephen Baccus · Christopher Ré
• 2022 Poster: Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference »
  Jasper Tan · Blake Mason · Hamid Javadi · Richard Baraniuk
• 2022 Poster: On the Importance of Gradient Norm in PAC-Bayesian Bounds »
  Itai Gat · Yossi Adi · Alex Schwing · Tamir Hazan
• 2022 Poster: Truncated proposals for scalable and hassle-free simulation-based inference »
  Michael Deistler · Pedro Goncalves · Jakob H Macke
• 2022 Poster: Extracting computational mechanisms from neural data using low-rank RNNs »
  Adrian Valente · Jonathan Pillow · Srdjan Ostojic
• 2022 Poster: Efficient identification of informative features in simulation-based inference »
  Jonas Beck · Michael Deistler · Yves Bernaerts · Jakob H Macke · Philipp Berens
• 2021 : Anima Anandkumar »
  Anima Anandkumar
• 2021 : Reinforcement Learning in Factored Action Spaces using Tensor Decompositions »
  Anuj Mahajan · Mikayel Samvelyan · Lei Mao · Viktor Makoviichuk · Animesh Garg · Jean Kossaifi · Shimon Whiteson · Yuke Zhu · Anima Anandkumar
• 2021 : General Discussion 2 - What does the OOD problem mean to you and your field? with Anima Anandkumar, Terry Sejnowski, Chris White: General Discussion 2 »
  Anima Anandkumar · Terry Sejnowski · Weiwei Yang · Joshua T Vogelstein
• 2021 : Anima Anandkumar: Role of AI in predicting and mitigating climate change »
  Anima Anandkumar
• 2021 : Live Q&A Session 1 with Yoshua Bengio, Leyla Isik, Konrad Kording, Bernhard Scholkopf, Amit Sharma, Joshua Vogelstein, Weiwei Yang »
  Yoshua Bengio · Leyla Isik · Konrad Kording · Bernhard Schölkopf · Joshua T Vogelstein · Weiwei Yang
• 2021 : Efficient Quantum Optimization via Multi-Basis Encodings and Tensor Rings »
  Anima Anandkumar
• 2021 : Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update »
  Jiawei Zhao · Steve Dai · Rangha Venkatesan · Brian Zimmer · Mustafa Ali · Ming-Yu Liu · Brucek Khailany · · Anima Anandkumar
• 2021 : Methods:: An Interactive Visualization Tool for Understanding Active Learning »
  Martin Ester · Jialin Lu
• 2021 : Session 3 | Invited talk: Surya Ganguli, "From the geometry of high dimensional energy landscapes to optimal annealing in a dissipative many body quantum optimizer" »
  Surya Ganguli · Atilim Gunes Baydin
• 2021 : Spotlight 5: Strategic clustering »
  Ana-Andreea Stoica · Christos Papadimitriou
• 2021 : Function-guided protein design by deep manifold sampling »
  Vladimir Gligorijevic · Stephen Ra · Dan Berenberg · Richard Bonneau · Kyunghyun Cho
• 2021 : Accelerating Systems and ML for Science »
  Anima Anandkumar
• 2021 Workshop: Causal Inference & Machine Learning: Why now? »
  Elias Bareinboim · Bernhard Schölkopf · Terrence Sejnowski · Yoshua Bengio · Judea Pearl
• 2021 Workshop: Machine Learning and the Physical Sciences »
  Anima Anandkumar · Kyle Cranmer · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Emine Kucukbenli · Gilles Louppe · Benjamin Nachman · Brian Nord · Savannah Thais
• 2021 Poster: Three-dimensional spike localization and improved motion correction for Neuropixels recordings »
  Julien Boussard · Erdem Varol · Hyun Dong Lee · Nishchal Dethe · Liam Paninski
• 2021 Poster: Bridging the Imitation Gap by Adaptive Insubordination »
  Luca Weihs · Unnat Jain · Iou-Jen Liu · Jordi Salvador · Svetlana Lazebnik · Aniruddha Kembhavi · Alex Schwing
• 2021 Poster: Estimating the Unique Information of Continuous Variables »
  Ari Pakman · Amin Nejatbakhsh · Dar Gilboa · Abdullah Makkeh · Luca Mazzucato · Michael Wibral · Elad Schneidman
• 2021 Poster: Per-Pixel Classification is Not All You Need for Semantic Segmentation »
  Bowen Cheng · Alex Schwing · Alexander Kirillov
• 2021 Poster: Zero Time Waste: Recycling Predictions in Early Exit Neural Networks »
  Maciej Wołczyk · Bartosz Wójcik · Klaudia Bałazy · Igor T Podolak · Jacek Tabor · Marek Śmieja · Tomasz Trzcinski
• 2021 : NaturalProofs: Mathematical Theorem Proving in Natural Language »
  Sean Welleck · Jiacheng Liu · Ronan Le Bras · Hanna Hajishirzi · Yejin Choi · Kyunghyun Cho
• 2021 Poster: Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks »
  Hidenori Tanaka · Daniel Kunin
• 2021 Poster: A Contrastive Learning Approach for Training Variational Autoencoder Priors »
  Jyoti Aneja · Alex Schwing · Jan Kautz · Arash Vahdat
• 2021 Poster: True Few-Shot Learning with Language Models »
  Ethan Perez · Douwe Kiela · Kyunghyun Cho
• 2021 Poster: Deep Learning on a Data Diet: Finding Important Examples Early in Training »
  Mansheej Paul · Surya Ganguli · Gintare Karolina Dziugaite
• 2021 Poster: Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks »
  Aran Nayebi · Alexander Attinger · Malcolm Campbell · Kiah Hardcastle · Isabel Low · Caitlin S Mallory · Gabriel Mel · Ben Sorscher · Alex H Williams · Surya Ganguli · Lisa Giocomo · Dan Yamins
• 2021 Poster: Towards robust vision by multi-task learning on monkey visual cortex »
  Shahd Safarani · Arne Nix · Konstantin Willeke · Santiago Cadena · Kelli Restivo · George Denfield · Andreas Tolias · Fabian Sinz
• 2021 Poster: The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization »
  Daniel LeJeune · Hamid Javadi · Richard Baraniuk
• 2021 Poster: Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning »
  Ekdeep S Lubana · Robert Dick · Hidenori Tanaka
• 2021 Poster: Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity »
  Gabriel Ocker · Michael Buice
• 2021 Poster: Local plasticity rules can learn deep representations using self-supervised contrastive predictions »
  Bernd Illing · Jean Ventura · Guillaume Bellec · Wulfram Gerstner
• 2021 : An Interactive Tool for Computation with Assemblies of Neurons »
  Seung Je Jung · Christos Papadimitriou · Santosh Vempala
• 2021 Poster: Continual World: A Robotic Benchmark For Continual Reinforcement Learning »
  Maciej Wołczyk · Michał Zając · Razvan Pascanu · Łukasz Kuciński · Piotr Miłoś
• 2021 Poster: Self-Supervised Learning with Kernel Dependence Maximization »
  Yazhe Li · Roman Pogodin · Danica J. Sutherland · Arthur Gretton
• 2021 Poster: Towards Biologically Plausible Convolutional Networks »
  Roman Pogodin · Yash Mehta · Timothy Lillicrap · Peter E Latham
• 2021 Poster: FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention »
  Tan Nguyen · Vai Suliafu · Stanley Osher · Long Chen · Bao Wang
• 2021 Poster: Heavy Ball Neural Ordinary Differential Equations »
  Hedi Xia · Vai Suliafu · Hangjie Ji · Tan Nguyen · Andrea Bertozzi · Stanley Osher · Bao Wang
• 2021 Poster: Class-agnostic Reconstruction of Dynamic Objects from Videos »
  Zhongzheng Ren · Xiaoming Zhao · Alex Schwing
• 2021 Poster: Perceptual Score: What Data Modalities Does Your Model Perceive? »
  Itai Gat · Idan Schwartz · Alex Schwing
• 2021 Poster: A flow-based latent state generative model of neural population responses to natural images »
  Mohammad Bashiri · Edgar Walker · Konstantin-Klemens Lurz · Akshay Jagadish · Taliah Muhammad · Zhiwei Ding · Zhuokun Ding · Andreas Tolias · Fabian Sinz
• 2021 Poster: Biological learning in key-value memory networks »
  Danil Tyulmankov · Ching Fang · Annapurna Vadaparty · Guangyu Robert Yang
• 2021 Poster: Non-Gaussian Gaussian Processes for Few-Shot Regression »
  Marcin Sendera · Jacek Tabor · Aleksandra Nowak · Andrzej Bedychaj · Massimiliano Patacchiola · Tomasz Trzcinski · Przemysław Spurek · Maciej Zieba
• 2021 Poster: Credit Assignment Through Broadcasting a Global Error Vector »
  David Clark · L F Abbott · Sueyeon Chung
• 2021 Poster: Neural optimal feedback control with local learning rules »
  Johannes Friedrich · Siavash Golkar · Shiva Farashahi · Alexander Genkin · Anirvan Sengupta · Dmitri Chklovskii
• 2021 Poster: Fitting summary statistics of neural data with a differentiable spiking network simulator »
  Guillaume Bellec · Shuqi Wang · Alireza Modirshanechi · Johanni Brea · Wulfram Gerstner
• 2021 Poster: Iterative Amortized Policy Optimization »
  Joseph Marino · Alexandre Piche · Alessandro Davide Ialongo · Yisong Yue
• 2020 : Climate Change and ML in the Private Sector »
  Aisha Walcott-Bryant · Lea Boche · Anima Anandkumar
• 2020 : Opening Remarks »
  Reinhard Heckel · Paul Hand · Soheil Feizi · Lenka Zdeborová · Richard Baraniuk
• 2020 Workshop: Workshop on Deep Learning and Inverse Problems »
  Reinhard Heckel · Paul Hand · Richard Baraniuk · Lenka Zdeborová · Soheil Feizi
• 2020 Workshop: Machine Learning and the Physical Sciences »
  Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais
• 2020 : Daniela Witten »
  Daniela M Witten
• 2020 Poster: Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs »
  Naganand Yadati
• 2020 Poster: Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel »
  Stanislav Fort · Gintare Karolina Dziugaite · Mansheej Paul · Sepideh Kharaghani · Daniel Roy · Surya Ganguli
• 2020 Poster: Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning »
  Zhongzheng Ren · Raymond A. Yeh · Alex Schwing
• 2020 Poster: Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks »
  Roman Pogodin · Peter E Latham
• 2020 Poster: A simple normative network approximates local non-Hebbian learning in the cortex »
  Siavash Golkar · David Lipshutz · Yanis Bahroun · Anirvan Sengupta · Dmitri Chklovskii
• 2020 Poster: Predictive coding in balanced neural networks with noise, chaos and delays »
  Jonathan Kadmon · Jonathan Timcheck · Surya Ganguli
• 2020 Poster: Towards a Better Global Loss Landscape of GANs »
  Ruoyu Sun · Tiantian Fang · Alex Schwing
• 2020 Poster: A Biologically Plausible Neural Network for Slow Feature Analysis »
  David Lipshutz · Charles Windolf · Siavash Golkar · Dmitri Chklovskii
• 2020 Poster: Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks »
  Randall Balestriero · Sebastien PARIS · Richard Baraniuk
• 2020 Poster: Identifying Learning Rules From Neural Network Observables »
  Aran Nayebi · Sanjana Srivastava · Surya Ganguli · Daniel Yamins
• 2020 Poster: Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses »
  Ronald (James) Cotton · Fabian Sinz · Andreas Tolias
• 2020 Poster: Learning compositional functions via multiplicative weight updates »
  Jeremy Bernstein · Jiawei Zhao · Markus Meister · Ming-Yu Liu · Anima Anandkumar · Yisong Yue
• 2020 Poster: Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations »
  Joshua Glaser · Matthew Whiteway · John Cunningham · Liam Paninski · Scott Linderman
• 2020 Spotlight: Identifying Learning Rules From Neural Network Observables »
  Aran Nayebi · Sanjana Srivastava · Surya Ganguli · Daniel Yamins
• 2020 Oral: Towards a Better Global Loss Landscape of GANs »
  Ruoyu Sun · Tiantian Fang · Alex Schwing
• 2020 Session: Orals & Spotlights Track 22: Vision Applications »
  Leonid Sigal · Alex Schwing
• 2020 Poster: MomentumRNN: Integrating Momentum into Recurrent Neural Networks »
  Tan Nguyen · Richard Baraniuk · Andrea Bertozzi · Stanley Osher · Bao Wang
• 2020 Poster: Evaluating Attribution for Graph Neural Networks »
  Benjamin Sanchez-Lengeling · Jennifer Wei · Brian Lee · Emily Reif · Peter Wang · Wesley Qian · Kevin McCloskey · Lucy Colwell · Alexander Wiltschko
• 2020 Poster: Neural Networks with Recurrent Generative Feedback »
  Yujia Huang · James Gornet · Sihui Dai · Zhiding Yu · Tan Nguyen · Doris Tsao · Anima Anandkumar
• 2020 Poster: Causal Discovery in Physical Systems from Videos »
  Yunzhu Li · Antonio Torralba · Anima Anandkumar · Dieter Fox · Animesh Garg
• 2020 Poster: Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies »
  Itai Gat · Idan Schwartz · Alex Schwing · Tamir Hazan
• 2020 Poster: Black-Box Optimization with Local Generative Surrogates »
  Sergey Shirobokov · Vladislav Belavin · Michael Kagan · Andrei Ustyuzhanin · Atilim Gunes Baydin
• 2020 Poster: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
  Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar
• 2020 Spotlight: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
  Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar
• 2020 : Research at NVIDIA: New Core AI and Machine Learning Lab »
  Anima Anandkumar
• 2020 Poster: Multipole Graph Neural Operator for Parametric Partial Differential Equations »
  Zongyi Li · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Andrew Stuart · Kaushik Bhattacharya · Anima Anandkumar
• 2020 Poster: High-Throughput Synchronous Deep RL »
  Iou-Jen Liu · Raymond A. Yeh · Alex Schwing
• 2020 Poster: Learning to Learn with Feedback and Local Plasticity »
  Jack Lindsey · Ashok Litwin-Kumar
• 2020 Poster: Convolutional Tensor-Train LSTM for Spatio-Temporal Learning »
  Jiahao Su · Wonmin Byeon · Jean Kossaifi · Furong Huang · Jan Kautz · Anima Anandkumar
• 2020 Poster: Learning Physical Graph Representations from Visual Scenes »
  Daniel Bear · Chaofei Fan · Damian Mrowca · Yunzhu Li · Seth Alter · Aran Nayebi · Jeremy Schwartz · Li Fei-Fei · Jiajun Wu · Josh Tenenbaum · Daniel Yamins
• 2020 Poster: Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems »
  Sahin Lale · Kamyar Azizzadenesheli · Babak Hassibi · Anima Anandkumar
• 2020 Poster: Pruning neural networks without any data by iteratively conserving synaptic flow »
  Hidenori Tanaka · Daniel Kunin · Daniel Yamins · Surya Ganguli
• 2020 Oral: Learning Physical Graph Representations from Visual Scenes »
  Daniel Bear · Chaofei Fan · Damian Mrowca · Yunzhu Li · Seth Alter · Aran Nayebi · Jeremy Schwartz · Li Fei-Fei · Jiajun Wu · Josh Tenenbaum · Daniel Yamins
• 2020 : Prof. Anima Anandkumar (California Institute of Technology and NVIDIA) »
  Anima Anandkumar
• 2019 : Panel Session: A new hope for neuroscience »
  Yoshua Bengio · Blake Richards · Timothy Lillicrap · Ila Fiete · David Sussillo · Doina Precup · Konrad Kording · Surya Ganguli
• 2019 : Panel - The Role of Communication at Large: Aparna Lakshmiratan, Jason Yosinski, Been Kim, Surya Ganguli, Finale Doshi-Velez »
  Aparna Lakshmiratan · Finale Doshi-Velez · Surya Ganguli · Zachary Lipton · Michela Paganini · Anima Anandkumar · Jason Yosinski
• 2019 : Coffee Break & Poster Session 2 »
  Juho Lee · Yoonho Lee · Yee Whye Teh · Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing · Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Christian Bueno · Aditya Sanghi · Pradeep Kumar Jayaraman · Ignacio Arroyo-Fernández · Andrew Hryniowski · Vinayak Mathur · Sanjay Singh · Shahrzad Haddadan · Vasco Portilheiro · Luna Zhang · Mert Yuksekgonul · Jhosimar Arias Figueroa · Deepak Maurya · Balaraman Ravindran · Frank NIELSEN · Philip Pham · Justin Payan · Andrew McCallum · Jinesh Mehta · Ke SUN
• 2019 : Poster Presentations »
  Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange
• 2019 : Invited Talk: Theories for the emergence of internal representations in neural networks: from perception to navigation »
  Surya Ganguli
• 2019 : Poster Session »
  Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn
• 2019 : Contributed Talk - Chirality Nets: Exploiting Structure in Human Pose Regression »
  Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing
• 2019 : Poster Session + Lunch »
  Maxwell Nye · Robert Kim · Toby St Clere Smithe · Takeshi D. Itoh · Omar U. Florez · Vesna G. Djokic · Sneha Aenugu · Mariya Toneva · Imanol Schlag · Dan Schwartz · Max Raphael Sobroza Marques · Pravish Sainath · Peng-Hsuan Li · Rishi Bommasani · Najoung Kim · Paul Soulos · Steven Frankland · Nadezhda Chirkova · Dongqi Han · Adam Kortylewski · Rich Pang · Milena Rabovsky · Jonathan Mamou · Vaibhav Kumar · Tales Marra
• 2019 : Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory »
  Robert Kim
• 2019 : Poster and Coffee Break 1 »
  Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova
• 2019 : Surya Ganguli, Yasaman Bahri, Florent Krzakala moderated by Lenka Zdeborova »
  Florent Krzakala · Yasaman Bahri · Surya Ganguli · Lenka Zdeborová · Adji Bousso Dieng · Joan Bruna
• 2019 : Opening Remarks »
  Guillaume Lajoie · Jessica Thompson · Maximilian Puelma Touzel · Eli Shlizerman · Konrad Kording
• 2019 : Surya Ganguli - An analytic theory of generalization dynamics and transfer learning in deep linear networks »
  Surya Ganguli
• 2019 : Opening Remarks »
  Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood
• 2019 Workshop: Machine Learning and the Physical Sciences »
  Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood
• 2019 Workshop: Emergent Communication: Towards Natural Language »
  Abhinav Gupta · Michael Noukhovitch · Cinjon Resnick · Natasha Jaques · Angelos Filos · Marie Ossenkopf · Angeliki Lazaridou · Jakob Foerster · Ryan Lowe · Douwe Kiela · Kyunghyun Cho
• 2019 Workshop: Context and Compositionality in Biological and Artificial Neural Systems »
  Javier Turek · Shailee Jain · Alexander Huth · Leila Wehbe · Emma Strubell · Alan Yuille · Tal Linzen · Christopher Honey · Kyunghyun Cho
• 2019 Workshop: Real Neurons & Hidden Units: future directions at the intersection of neuroscience and AI »
  Guillaume Lajoie · Eli Shlizerman · Maximilian Puelma Touzel · Jessica Thompson · Konrad Kording
• 2019 : Poster session »
  Jindong Gu · Alice Xiang · Atoosa Kasirzadeh · Zhiwei Han · Omar U. Florez · Frederik Harder · An-phi Nguyen · Amir Hossein Akhavan Rahnama · Michele Donini · Dylan Slack · Junaid Ali · Paramita Koley · Michiel Bakker · Anna Hilgard · Hailey James · Gonzalo Ramos · Jialin Lu · Jingying Yang · Margarita Boyarskaya · Martin Pawelczyk · Kacper Sokol · Mimansa Jaiswal · Umang Bhatt · David Alvarez-Melis · Aditya Grover · Charles Marx · Mengjiao (Sherry) Yang · Jingyan Wang · Gökhan Çapan · Hanchen Wang · Steffen Grünewälder · Moein Khajehnejad · Gourab Patro · Russell Kunes · Samuel Deng · Yuanting Liu · Luca Oneto · Mengze Li · Thomas Weber · Stefan Matthes · Duy Patrick Tu
• 2019 : Poster Session »
  Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis
• 2019 : Poster Session »
  Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie
• 2019 : Posters »
  Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner
• 2019 : Poster Session »
  Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou
• 2019 : Poster Session #1 »
  Adarsh Jamadandi · Sophia Sanborn · Huaxiu Yao · Chen Cai · Yu Chen · Jean-Marc Andreoli · Niklas Stoehr · Shih-Yang Su · Tony Duan · Fábio Ferreira · Davide Belli · Amit Boyarski · Ze Ye · Elahe Ghalebi · Arindam Sarkar · MAHMOUD KHADEMI · Evgeniy Faerman · Joey Bose · Jiaqi Ma · Lin Meng · Seyed Mehran Kazemi · Guangtao Wang · Tong Wu · Yuexin Wu · Chaitanya K. Joshi · Marc Brockschmidt · Daniele Zambon · Colin Graber · Rafaël Van Belle · Osman Asif Malik · Xavier Glorot · Mario Krenn · Chris Cameron · Binxuan Huang · George Stoica · Alexia Toumpa
• 2019 : Poster session »
  Michael Melese Woldeyohannis · Bernardt Duvenhage · Nyamos Waigama · Asaye Bir Senay · Claire Babirye · Tensaye Ayalew · Kelechi Ogueji · Vinay Prabhu · Prabu Ravindran · Fadilulah Wahab · ChukwuNonso H Nwokoye · Paul Duckworth · Hafte Abera · Abebe Mideksa · Loubna Benabbou · Anugraha Sinha · Ivan Kiskin · Robert Soden · Tupokigwe Isagah · Rehema Mwawado · Yimer Mohammed · Bryan Wilder · Daniel Omeiza · Sunayana Rane · Richard Mgaya · Samsun Knight · Jessenia Gonzalez Villarreal · Eyob Beyene · Monika Obrocka Tulinska · Luis Fernando Cantu Diaz de Leon · Joseph Aro · Michael T Smith · Michael Famoroti · Praneeth Vepakomma · Ramesh Raskar · Debjani Bhowmick · Chukwunonso H Nwokoye · Alejandro Noriega Campero · Hope Mbelwa · Anusua Trivedi
• 2019 : Contributed Talk #2: Slow processes of neurons enable a biologically plausible approximation to policy gradient »
  Wolfgang Maass
• 2019 : Coffee Break & Poster Session »
  Samia Mohinta · Andrea Agostinelli · Alexandra Moringen · Jee Hang Lee · Yat Long Lo · Wolfgang Maass · Blue Sheffer · Colin Bredenberg · Benjamin Eysenbach · Liyu Xia · Efstratios Markou · Jan Lichtenberg · Pierre Richemond · Tony Zhang · JB Lanier · Baihan Lin · William Fedus · Glen Berseth · Marta Sarrico · Matthew Crosby · Stephen McAleer · Sina Ghiassian · Franz Scherr · Guillaume Bellec · Darjan Salaj · Arinbjörn Kolbeinsson · Matthew Rosenberg · Jaehoon Shin · Sang Wan Lee · Guillermo Cecchi · Irina Rish · Elias Hajek
• 2019 : Poster session »
  Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak
• 2019 : Opening Remarks »
  Reinhard Heckel · Paul Hand · Alex Dimakis · Joan Bruna · Deanna Needell · Richard Baraniuk
• 2019 Workshop: Solving inverse problems with deep networks: New architectures, theoretical foundations, and applications »
  Reinhard Heckel · Paul Hand · Richard Baraniuk · Joan Bruna · Alex Dimakis · Deanna Needell
• 2019 Poster: Chirality Nets for Human Pose Regression »
  Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing
• 2019 Poster: Graph Structured Prediction Energy Networks »
  Colin Graber · Alex Schwing
• 2019 Poster: Can Unconditional Language Models Recover Arbitrary Sentences? »
  Nishant Subramani · Samuel Bowman · Kyunghyun Cho
• 2019 Poster: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs »
  Jonas Kubilius · Martin Schrimpf · Kohitij Kar · Rishi Rajalingham · Ha Hong · Najib Majaj · Elias Issa · Pouya Bashivan · Jonathan Prescott-Roy · Kailyn Schmidt · Aran Nayebi · Daniel Bear · Daniel Yamins · James J DiCarlo
• 2019 Poster: Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex »
  Jianghong Shi · Eric Shea-Brown · Michael Buice
• 2019 Poster: Competitive Gradient Descent »
  Florian Schaefer · Anima Anandkumar
• 2019 Poster: Learning from brains how to regularize machines »
  Zhe Li · Wieland Brendel · Edgar Walker · Erick Cobos · Taliah Muhammad · Jacob Reimer · Matthias Bethge · Fabian Sinz · Xaq Pitkow · Andreas Tolias
• 2019 Poster: A Bayesian Theory of Conformity in Collective Decision Making »
  Koosha Khalvati · Saghar Mirbagheri · Seongmin A. Park · Jean-Claude Dreher · Rajesh PN Rao
• 2019 Poster: BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos »
  Eleanor Batty · Matthew Whiteway · Shreya Saxena · Dan Biderman · Taiga Abe · Simon Musall · Winthrop Gillis · Jeffrey Markowitz · Anne Churchland · John Cunningham · Sandeep R Datta · Scott Linderman · Liam Paninski
• 2019 Poster: A unified theory for the origin of grid cells through the lens of pattern formation »
  Ben Sorscher · Gabriel Mel · Surya Ganguli · Samuel Ocko
• 2019 Poster: Universality and individuality in neural dynamics across large populations of recurrent networks »
  Niru Maheswaranathan · Alex Williams · Matthew Golub · Surya Ganguli · David Sussillo
• 2019 Spotlight: A unified theory for the origin of grid cells through the lens of pattern formation »
  Ben Sorscher · Gabriel Mel · Surya Ganguli · Samuel Ocko
• 2019 Spotlight: Universality and individuality in neural dynamics across large populations of recurrent networks »
  Niru Maheswaranathan · Alex Williams · Matthew Golub · Surya Ganguli · David Sussillo
• 2019 Oral: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs »
  Jonas Kubilius · Martin Schrimpf · Ha Hong · Najib Majaj · Rishi Rajalingham · Elias Issa · Kohitij Kar · Pouya Bashivan · Jonathan Prescott-Roy · Kailyn Schmidt · Aran Nayebi · Daniel Bear · Daniel Yamins · James J DiCarlo
• 2019 Poster: Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models »
  Ruoxi Sun · Ian Kinsella · Scott Linderman · Liam Paninski
• 2019 Poster: HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs »
  Naganand Yadati · Madhav Nimishakavi · Prateek Yadav · Vikram Nitin · Anand Louis · Partha Talukdar
• 2019 Poster: From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction »
  Hidenori Tanaka · Aran Nayebi · Niru Maheswaranathan · Lane McIntosh · Stephen Baccus · Surya Ganguli
• 2019 Poster: Efficient characterization of electrically evoked responses for neural interfaces »
  Nishal Shah · Sasidhar Madugula · Pawel Hottowy · Alexander Sher · Alan Litke · Liam Paninski · E.J. Chichilnisky
• 2019 Poster: TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines »
  Jingxiang Lin · Unnat Jain · Alex Schwing
• 2019 Poster: Co-Generation with GANs using AIS based HMC »
  Tiantian Fang · Alex Schwing
• 2019 Oral: Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models »
  Ruoxi Sun · Ian Kinsella · Scott Linderman · Liam Paninski
• 2019 Poster: Deep Learning without Weight Transport »
  Mohamed Akrout · Collin Wilson · Peter Humphreys · Timothy Lillicrap · Douglas Tweed
• 2019 Poster: Intrinsic dimension of data representations in deep neural networks »
  Alessio Ansuini · Alessandro Laio · Jakob H Macke · Davide Zoccolan
• 2019 Poster: Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics »
  Niru Maheswaranathan · Alex Williams · Matthew Golub · Surya Ganguli · David Sussillo
• 2019 Poster: The Geometry of Deep Networks: Power Diagram Subdivision »
  Randall Balestriero · Romain Cosentino · Behnaam Aazhang · Richard Baraniuk
• 2019 Tutorial: Imitation Learning and its Application to Natural Language Generation »
  Kyunghyun Cho · Hal Daumé III
• 2018 : Poster session »
  David Zeng · Marzieh S. Tahaei · Shuai Chen · Felix Meister · Meet Shah · Anant Gupta · Ajil Jalal · Eirini Arvaniti · David Zimmerer · Konstantinos Kamnitsas · Pedro Ballester · Nathaniel Braman · Udaya Kumar · Sil C. van de Leemput · Junaid Qadir · Hoel Kervadec · Mohamed Akrout · Adrian Tousignant · Matthew Ng · Raghav Mehta · Miguel Monteiro · Sumana Basu · Jonas Adler · Adrian Dalca · Jizong Peng · Sungyeob Han · Xiaoxiao Li · Karthik Gopinath · Joseph Cheng · Bogdan Georgescu · Kha Gia Quach · Karthik Sarma · David Van Veen
• 2018 : Contributed Talk 3 »
  Tan Nguyen
• 2018 Workshop: Emergent Communication Workshop »
  Jakob Foerster · Angeliki Lazaridou · Ryan Lowe · Igor Mordatch · Douwe Kiela · Kyunghyun Cho
• 2018 Workshop: Integration of Deep Learning Theories »
  Richard Baraniuk · Anima Anandkumar · Stephane Mallat · Ankit Patel · nhật Hồ
• 2018 : Panel Discussion »
  Richard Baraniuk · Maarten V. de Hoop · Paul A Johnson
• 2018 : Introduction »
  Laura Pyrak-Nolte · James Rustad · Richard Baraniuk
• 2018 Workshop: Machine Learning for Geophysical & Geochemical Signals »
  Laura Pyrak-Nolte · James Rustad · Richard Baraniuk
• 2018 Poster: Loss Functions for Multiset Prediction »
  Sean Welleck · Zixin Yao · Yu Gai · Jialin Mao · Zheng Zhang · Kyunghyun Cho
• 2018 Poster: Processing of missing data by neural networks »
  Marek Śmieja · Łukasz Struski · Jacek Tabor · Bartosz Zieliński · Przemysław Spurek
• 2018 Poster: Deep Structured Prediction with Nonlinear Output Transformations »
  Colin Graber · Ofer Meshi · Alex Schwing
• 2018 Poster: Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons »
  Nima Anari · Constantinos Daskalakis · Wolfgang Maass · Christos Papadimitriou · Amin Saberi · Santosh Vempala
• 2018 Poster: Long short-term memory and Learning-to-learn in networks of spiking neurons »
  Guillaume Bellec · Darjan Salaj · Anand Subramoney · Robert Legenstein · Wolfgang Maass
• 2018 Poster: Gradient Descent for Spiking Neural Networks »
  Dongsung Huh · Terrence Sejnowski
• 2018 Poster: The emergence of multiple retinal cell types through efficient coding of natural movies »
  Samuel Ocko · Jack Lindsey · Surya Ganguli · Stephane Deny
• 2018 Poster: Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training »
  Youjie Li · Mingchao Yu · Songze Li · Salman Avestimehr · Nam Sung Kim · Alex Schwing
• 2018 Poster: Statistical mechanics of low-rank tensor decomposition »
  Jonathan Kadmon · Surya Ganguli
• 2018 Poster: Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering »
  Medhini Narasimhan · Svetlana Lazebnik · Alex Schwing
• 2018 Poster: Task-Driven Convolutional Recurrent Models of the Visual System »
  Aran Nayebi · Daniel Bear · Jonas Kubilius · Kohitij Kar · Surya Ganguli · David Sussillo · James J DiCarlo · Daniel Yamins
• 2018 Poster: Stimulus domain transfer in recurrent models for large scale cortical population prediction on video »
  Fabian Sinz · Alexander Ecker · Paul Fahey · Edgar Walker · Erick M Cobos · Emmanouil Froudarakis · Dimitri Yatsenko · Xaq Pitkow · Jacob Reimer · Andreas Tolias
• 2018 Poster: GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training »
  Mingchao Yu · Zhifeng Lin · Krishna Narra · Songze Li · Youjie Li · Nam Sung Kim · Alex Schwing · Murali Annavaram · Salman Avestimehr
• 2018 Poster: A General Method for Amortizing Variational Filtering »
  Joseph Marino · Milan Cvitkovic · Yisong Yue
• 2017 : Machine vision and learning for extracting a mechanistic understanding of neural computation »
  Kristin Branson
• 2017 : POSTER: Pre-training attentional mechanisms »
  Jack Lindsey
• 2017 : Machine learning for cognitive mapping »
  Gaël Varoquaux
• 2017 : Discovery of governing equations and biological principles from spatio-temporal time-series recordings »
  Nathan Kutz
• 2017 : Panel on "What neural systems can teach us about building better machine learning systems" »
  Timothy Lillicrap · James J DiCarlo · Christopher Rozell · Viren Jain · Nathan Kutz · William Gray Roncal · Bingni Brunton
• 2017 : Doris Tsao »
  Doris Tsao
• 2017 Workshop: Emergent Communication Workshop »
  Jakob Foerster · Igor Mordatch · Angeliki Lazaridou · Kyunghyun Cho · Douwe Kiela · Pieter Abbeel
• 2017 : DeepArt competition »
  Alexander Ecker · Leon A Gatys · Matthias Bethge
• 2017 : Poster Session »
  Shunsuke Horii · Heejin Jeong · Tobias Schwedes · Qing He · Ben Calderhead · Ertunc Erdil · Jaan Altosaar · Patrick Muchmore · Rajiv Khanna · Ian Gemp · Pengfei Zhang · Yuan Zhou · Chris Cremer · Maria DeYoreo · Alexander Terenin · Brendan McVeigh · Rachit Singh · Yaodong Yang · Erik Bodin · Trefor Evans · Henry Chai · Shandian Zhe · Jeffrey Ling · Vincent ADAM · Lars Maaløe · Andrew Miller · Ari Pakman · Josip Djolonga · Hong Ge
• 2017 : Poster Session 1 »
  Magdalena Fuchs · David Lung · Mathias Lechner · Kezhi Li · Andrew Gordus · Vivek Venkatachalam · Shivesh Chaudhary · Jan Hůla · David Rolnick · Scott Linderman · Gonzalo Mena · Liam Paninski · Netta Cohen
• 2017 : Poster Spotlights »
  Francesco Locatello · Ari Pakman · Da Tang · Thomas Rainforth · Zalan Borsos · Marko Järvenpää · Eric Nalisnick · Gabriele Abbati · XIAOYU LU · Jonathan Huggins · Rachit Singh · Rui Luo
• 2017 Workshop: Advances in Modeling and Learning Interactions from Complex Data »
  Gautam Dasarathy · Mladen Kolar · Richard Baraniuk
• 2017 Spotlight: Deep Networks for Decoding Natural Images from Retinal Signals »
  Nikhil Parthasarathy · Eleanor Batty · William Falcon · Thomas Rutten · Mohit Rajpal · E.J. Chichilnisky · Liam Paninski
• 2017 Spotlight: Fast amortized inference of neural activity from calcium imaging data with variational autoencoders »
  Artur Speiser · Jinyao Yan · Evan Archer · Lars Buesing · Srinivas C Turaga · Jakob H Macke
• 2017 Poster: Learning to Pivot with Adversarial Networks »
  Gilles Louppe · Michael Kagan · Kyle Cranmer
• 2017 Poster: Fast amortized inference of neural activity from calcium imaging data with variational autoencoders »
  Artur Speiser · Jinyao Yan · Evan Archer · Lars Buesing · Srinivas C Turaga · Jakob H Macke
• 2017 Poster: Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net »
  Anirudh Goyal · Nan Rosemary Ke · Surya Ganguli · Yoshua Bengio
• 2017 Poster: Dualing GANs »
  Yujia Li · Alex Schwing · Kuan-Chieh Wang · Richard Zemel
• 2017 Poster: Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons »
  Nikhil Parthasarathy · Eleanor Batty · William Falcon · Thomas Rutten · Mohit Rajpal · E.J. Chichilnisky · Liam Paninski
• 2017 Spotlight: Dualing GANs »
  Yujia Li · Alex Schwing · Kuan-Chieh Wang · Richard Zemel
• 2017 Poster: MaskRNN: Instance Level Video Object Segmentation »
  Yuan-Ting Hu · Jia-Bin Huang · Alex Schwing
• 2017 Poster: Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts »
  Raymond A. Yeh · Jinjun Xiong · Wen-Mei Hwu · Minh Do · Alex Schwing
• 2017 Poster: OnACID: Online Analysis of Calcium Imaging Data in Real Time »
  Andrea Giovannucci · Johannes Friedrich · Matt Kaufman · Anne Churchland · Dmitri Chklovskii · Liam Paninski · Eftychios Pnevmatikakis
• 2017 Poster: On the Complexity of Learning Neural Networks »
  Le Song · Santosh Vempala · John Wilmes · Bo Xie
• 2017 Poster: Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations »
  Marcel Nonnenmacher · Srinivas C Turaga · Jakob H Macke
• 2017 Poster: Asynchronous Parallel Coordinate Minimization for MAP Inference »
  Ofer Meshi · Alex Schwing
• 2017 Oral: Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts »
  Raymond A. Yeh · Jinjun Xiong · Wen-Mei Hwu · Minh Do · Alex Schwing
• 2017 Spotlight: On the Complexity of Learning Neural Networks »
  Le Song · Santosh Vempala · John Wilmes · Bo Xie
• 2017 Poster: Flexible statistical inference for mechanistic models of neural dynamics »
  Jan-Matthis Lueckmann · Pedro Goncalves · Giacomo Bassetto · Kaan Öcal · Marcel Nonnenmacher · Jakob H Macke
• 2017 Poster: Learned D-AMP: Principled Neural Network based Compressive Image Recovery »
  Chris Metzler · Ali Mousavi · Richard Baraniuk
• 2017 Poster: Neural system identification for large populations separating “what” and “where” »
  David Klindt · Alexander Ecker · Thomas Euler · Matthias Bethge
• 2017 Poster: High-Order Attention Models for Visual Question Answering »
  Idan Schwartz · Alex Schwing · Tamir Hazan
• 2017 Poster: YASS: Yet Another Spike Sorter »
  Jin Hyung Lee · David Carlson · Hooshmand Shokri Razaghi · Weichi Yao · Georges A Goetz · Espen Hagen · Eleanor Batty · E.J. Chichilnisky · Gaute T. Einevoll · Liam Paninski
• 2017 Poster: Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice »
  Jeffrey Pennington · Samuel Schoenholz · Surya Ganguli
• 2017 Poster: Saliency-based Sequential Image Attention with Multiset Prediction »
  Sean Welleck · Jialin Mao · Kyunghyun Cho · Zheng Zhang
• 2017 Poster: Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space »
  Liwei Wang · Alex Schwing · Svetlana Lazebnik
• 2016 : Anima Anandkumar »
  Anima Anandkumar
• 2016 : From Brains to Bits and Back Again »
  Yoshua Bengio · Terrence Sejnowski · Christos H Papadimitriou · Jakob H Macke · Demis Hassabis · Alyson Fletcher · Andreas Tolias · Jascha Sohl-Dickstein · Konrad P Koerding
• 2016 : Surya Ganguli : Deep Neural Models of the Retinal Response to Natural Stimuli »
  Surya Ganguli
• 2016 : Reward-based self-configuration of networks of spiking neurons »
  Wolfgang Maass
• 2016 Workshop: Learning with Tensors: Why Now and How? »
  Anima Anandkumar · Rong Ge · Yan Liu · Maximilian Nickel · Qi (Rose) Yu
• 2016 Workshop: Machine Learning for Education »
  Richard Baraniuk · Jiquan Ngiam · Christoph Studer · Phillip Grimaldi · Andrew Lan
• 2016 : Stefan Mihalas : Modeling Optimal Context Integration in Cortical Columns »
  Stefan Mihalas
• 2016 : Non-convexity in the error landscape and the expressive capacity of deep neural networks »
  Surya Ganguli
• 2016 : Rajesh Rao - Modeling human decision making using POMDPs »
  Rajesh PN Rao
• 2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
  Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska
• 2016 Workshop: Representation Learning in Artificial and Biological Neural Networks »
  Leila Wehbe · Marcel Van Gerven · Moritz Grosse-Wentrup · Irina Rish · Brian Murphy · Georg Langs · Guillermo Cecchi · Anwar O Nunez-Elizalde
• 2016 Workshop: Brains and Bits: Neuroscience meets Machine Learning »
  Alyson Fletcher · Eva Dyer · Jascha Sohl-Dickstein · Joshua T Vogelstein · Konrad Koerding · Jakob H Macke
• 2016 Oral: Dense Associative Memory for Pattern Recognition »
  Dmitry Krotov · John J. Hopfield
• 2016 Invited Talk: Learning About the Brain: Neuroimaging and Beyond »
  Irina Rish
• 2016 Poster: End-to-End Goal-Driven Web Navigation »
  Rodrigo Nogueira · Kyunghyun Cho
• 2016 Poster: A Probabilistic Framework for Deep Learning »
  Ankit Patel · Tan Nguyen · Richard Baraniuk
• 2016 Poster: Linear dynamical neural population models through nonlinear embeddings »
  Yuanjun Gao · Evan Archer · Liam Paninski · John Cunningham
• 2016 Poster: High resolution neural connectivity from incomplete tracing data using nonnegative spline regression »
  Kameron Harris · Stefan Mihalas · Eric Shea-Brown
• 2016 Poster: Dense Associative Memory for Pattern Recognition »
  Dmitry Krotov · John J. Hopfield
• 2016 Poster: Iterative Refinement of the Approximate Posterior for Directed Belief Networks »
  R Devon Hjelm · Russ Salakhutdinov · Kyunghyun Cho · Nebojsa Jojic · Vince Calhoun · Junyoung Chung
• 2016 Poster: Fast Active Set Methods for Online Spike Inference from Calcium Imaging »
  Johannes Friedrich · Liam Paninski
• 2016 Poster: Constraints Based Convex Belief Propagation »
  Yaniv Tenzer · Alex Schwing · Kevin Gimpel · Tamir Hazan
• 2016 Poster: Exponential expressivity in deep neural networks through transient chaos »
  Ben Poole · Subhaneil Lahiri · Maithra Raghu · Jascha Sohl-Dickstein · Surya Ganguli
• 2016 Poster: Learning Deep Parsimonious Representations »
  Renjie Liao · Alex Schwing · Richard Zemel · Raquel Urtasun
• 2016 Poster: An equivalence between high dimensional Bayes optimal inference and M-estimation »
  Madhu Advani · Surya Ganguli
• 2016 Poster: Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity »
  Eugene Belilovsky · Gaël Varoquaux · Matthew Blaschko
• 2016 Oral: Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity »
  Eugene Belilovsky · Gaël Varoquaux · Matthew Blaschko
• 2016 Poster: A Probabilistic Model of Social Decision Making based on Reward Maximization »
  Koosha Khalvati · Seongmin A. Park · Jean-Claude Dreher · Rajesh PN Rao
• 2016 Poster: Online and Differentially-Private Tensor Decomposition »
  Yining Wang · Anima Anandkumar
• 2016 Poster: Deep Learning Models of the Retinal Response to Natural Scenes »
  Lane McIntosh · Niru Maheswaranathan · Aran Nayebi · Surya Ganguli · Stephen Baccus
• 2016 Poster: Automated scalable segmentation of neurons from multispectral images »
  Uygar Sümbül · Douglas Roossien · Dawen Cai · Fei Chen · Nicholas Barry · John Cunningham · Edward Boyden · Liam Paninski
• 2015 : Low-dimensional inference with high-dimensional data »
  Richard Baraniuk
• 2015 : Probabilistic Theory of Deep Learning »
  Richard Baraniuk
• 2015 : Methods overview: Studying the function and structure of microcircuits »
  Andreas Tolias
• 2015 : Opening and Overview »
  Anima Anandkumar
• 2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
  Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh
• 2015 : Correlations and Signatures of Criticality in Neural Population Models »
  Jakob H Macke
• 2015 Workshop: Machine Learning and Interpretation in Neuroimaging (day 1) »
  Irina Rish · Leila Wehbe · Brian Murphy · Georg Langs · Guillermo Cecchi · Moritz Grosse-Wentrup
• 2015 Workshop: Multimodal Machine Learning »
  Louis-Philippe Morency · Tadas Baltrusaitis · Aaron Courville · Kyunghyun Cho
• 2015 Workshop: Statistical Methods for Understanding Neural Systems »
  Alyson Fletcher · Jakob H Macke · Ryan Adams · Jascha Sohl-Dickstein
• 2015 Poster: Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring »
  David Kappel · Stefan Habenschuss · Robert Legenstein · Wolfgang Maass
• 2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
  Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar
• 2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
  Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar
• 2015 Poster: Attention-Based Models for Speech Recognition »
  Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio
• 2015 Poster: Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments »
  Dane Corneil · Wulfram Gerstner
• 2015 Oral: Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments »
  Dane Corneil · Wulfram Gerstner
• 2015 Spotlight: Attention-Based Models for Speech Recognition »
  Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio
• 2015 Poster: Unlocking neural population non-stationarities using hierarchical dynamics models »
  Mijung Park · Gergo Bohner · Jakob H Macke
• 2015 Poster: Sample Complexity of Learning Mahalanobis Distance Metrics »
  Nakul Verma · Kristin Branson
• 2015 Poster: Texture Synthesis Using Convolutional Neural Networks »
  Leon A Gatys · Alexander Ecker · Matthias Bethge
• 2015 Poster: Smooth and Strong: MAP Inference with Linear Convergence »
  Ofer Meshi · Mehrdad Mahdavi · Alex Schwing
• 2015 Poster: Deep Knowledge Tracing »
  Chris Piech · Jonathan Bassen · Jonathan Huang · Surya Ganguli · Mehran Sahami · Leonidas Guibas · Jascha Sohl-Dickstein
• 2015 Poster: A Bayesian Framework for Modeling Confidence in Perceptual Decision Making »
  Koosha Khalvati · Rajesh PN Rao
• 2014 Workshop: Human Propelled Machine Learning »
  Richard Baraniuk · Michael Mozer · Divyanshu Vats · Christoph Studer · Andrew E Waters · Andrew Lan
• 2014 Workshop: MLINI 2014 - 4th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner »
  Irina Rish · Georg Langs · Brian Murphy · Guillermo Cecchi · Kai-min K Chang · Leila Wehbe
• 2014 Workshop: Large scale optical physiology: From data-acquisition to models of neural coding »
  Il Memming Park · Jakob H Macke · Ferran Diego Andilla · Eftychios Pnevmatikakis · Jeremy Freeman
• 2014 Workshop: Deep Learning and Representation Learning »
  Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz
• 2014 Poster: Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials »
  Shenlong Wang · Alex Schwing · Raquel Urtasun
• 2014 Poster: Message Passing Inference for Large Scale Graphical Models with High Order Potentials »
  Jian Zhang · Alex Schwing · Raquel Urtasun
• 2014 Poster: Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition »
  Hanie Sedghi · Anima Anandkumar · Edmond A Jonckheere
• 2014 Poster: Non-convex Robust PCA »
  Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain
• 2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
  Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio
• 2014 Invited Talk: Using the Emergent Dynamics of Attractor Networks for Computation »
  John J. Hopfield
• 2014 Spotlight: Non-convex Robust PCA »
  Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain
• 2014 Poster: A Bayesian model for identifying hierarchically organised states in neural population activity »
  Patrick Putzky · Florian Franzen · Giacomo Bassetto · Jakob H Macke
• 2014 Poster: Clustered factor analysis of multineuronal spike data »
  Lars Buesing · Timothy A Machado · John P Cunningham · Liam Paninski
• 2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
  Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio
• 2014 Demonstration: Neural Machine Translation »
  Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio
• 2014 Spotlight: A Bayesian model for identifying hierarchically organised states in neural population activity »
  Patrick Putzky · Florian Franzen · Giacomo Bassetto · Jakob H Macke
• 2014 Spotlight: Clustered factor analysis of multineuronal spike data »
  Lars Buesing · Timothy A Machado · John P Cunningham · Liam Paninski
• 2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
  Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio
• 2014 Poster: Low-dimensional models of neural population activity in sensory cortical circuits »
  Evan Archer · Urs Koster · Jonathan W Pillow · Jakob H Macke
• 2014 Poster: Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks »
  Yanping Huang · Rajesh PN Rao
• 2013 Workshop: Acquiring and Analyzing the Activity of Large Neural Ensembles »
  Srinivas C Turaga · Lars Buesing · Maneesh Sahani · Jakob H Macke
• 2013 Workshop: Topic Models: Computation, Application, and Evaluation »
  David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar
• 2013 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2) »
  Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen
• 2013 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1) »
  Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen
• 2013 Poster: A multi-agent control framework for co-adaptation in brain-computer interfaces »
  Josh S Merel · Roy Fox · Tony Jebara · Liam Paninski
• 2013 Poster: Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits »
  Ben Shababo · Brooks Paige · Ari Pakman · Liam Paninski
• 2013 Spotlight: Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits »
  Ben Shababo · Brooks Paige · Ari Pakman · Liam Paninski
• 2013 Poster: Inferring neural population dynamics from multiple partial recordings of the same neural circuit »
  Srinivas C Turaga · Lars Buesing · Adam M Packer · Henry Dalgleish · Noah Pettit · Michael Hausser · Jakob H Macke
• 2013 Poster: A memory frontier for complex synapses »
  Subhaneil Lahiri · Surya Ganguli
• 2013 Poster: Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions »
  Ari Pakman · Liam Paninski
• 2013 Spotlight: Inferring neural population dynamics from multiple partial recordings of the same neural circuit »
  Srinivas C Turaga · Lars Buesing · Adam M Packer · Henry Dalgleish · Noah Pettit · Michael Hausser · Jakob H Macke
• 2013 Oral: A memory frontier for complex synapses »
  Subhaneil Lahiri · Surya Ganguli
• 2013 Session: Oral Session 3 »
  Terrence Sejnowski
• 2013 Poster: Latent Structured Active Learning »
  Wenjie Luo · Alex Schwing · Raquel Urtasun
• 2013 Poster: When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements »
  Divyanshu Vats · Richard Baraniuk
• 2013 Poster: Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions »
  Eftychios Pnevmatikakis · Liam Paninski
• 2013 Poster: When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity »
  Anima Anandkumar · Daniel Hsu · Majid Janzamin · Sham M Kakade
• 2013 Poster: Robust learning of low-dimensional dynamics from large neural ensembles »
  David Pfau · Eftychios Pnevmatikakis · Liam Paninski
• 2012 Workshop: MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day) »
  Georg Langs · Irina Rish · Guillermo Cecchi · Brian Murphy · Bjoern Menze · Kai-min K Chang · Moritz Grosse-Wentrup
• 2012 Workshop: MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day) »
  Georg Langs · Irina Rish · Guillermo Cecchi · Brian Murphy · Bjoern Menze · Kai-min K Chang · Moritz Grosse-Wentrup
• 2012 Poster: How Prior Probability Influences Decision Making: A Unifying Probabilistic Model »
  Yanping Huang · Abram Friesen · Timothy Hanks · Michael N Shadlen · Rajesh PN Rao
• 2012 Invited Talk: Suspicious Coincidences in the Brain »
  Terrence Sejnowski
• 2012 Poster: Learning Mixtures of Tree Graphical Models »
  Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade
• 2012 Poster: A Spectral Algorithm for Latent Dirichlet Allocation »
  Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu
• 2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
  Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu
• 2012 Poster: Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs »
  Anima Anandkumar · Ragupathyraj Valluvan
• 2012 Poster: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
  Lars Buesing · Jakob H Macke · Maneesh Sahani
• 2012 Poster: Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins »
  Alex Schwing · Tamir Hazan · Marc Pollefeys · Raquel Urtasun
• 2012 Oral: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
  Lars Buesing · Jakob H Macke · Maneesh Sahani
• 2011 Workshop: Machine Learning and Interpretation in Neuroimaging (MLINI-2011) »
  Melissa K Carroll · Guillermo Cecchi · Kai-min K Chang · Moritz Grosse-Wentrup · James Haxby · Georg Langs · Anna Korhonen · Bjoern Menze · Brian Murphy · Janaina Mourao-Miranda · Vittorio Murino · Francisco Pereira · Irina Rish · Mert Sabuncu · Irina Simanova · Bertrand Thirion
• 2011 Oral: Empirical models of spiking in neural populations »
  Jakob H Macke · Lars Buesing · John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani
• 2011 Poster: Empirical models of spiking in neural populations »
  Jakob H Macke · Lars Buesing · John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani
• 2011 Poster: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
  Animashree Anandkumar · Vincent Tan · Alan S Willsky
• 2011 Poster: Variational Learning for Recurrent Spiking Networks »
  Danilo J Rezende · Daan Wierstra · Wulfram Gerstner
• 2011 Oral: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
  Animashree Anandkumar · Vincent Tan · Alan S Willsky
• 2011 Poster: Information Rates and Optimal Decoding in Large Neural Populations »
  Kamiar Rahnama Rad · Liam Paninski
• 2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
  Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang
• 2011 Spotlight: Information Rates and Optimal Decoding in Large Neural Populations »
  Kamiar Rahnama Rad · Liam Paninski
• 2011 Poster: From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models »
  Skander Mensi · Richard Naud · Wulfram Gerstner
• 2011 Poster: How biased are maximum entropy models? »
  Jakob H Macke · Iain Murray · Peter E Latham
• 2011 Poster: SpaRCS: Recovering low-rank and sparse matrices from compressive measurements »
  Andrew E Waters · Aswin C Sankaranarayanan · Richard Baraniuk
• 2011 Session: Opening Remarks and Awards »
  Terrence Sejnowski · Peter Bartlett · Fernando Pereira
• 2010 Workshop: Practical Application of Sparse Modeling: Open Issues and New Directions »
  Irina Rish · Alexandru Niculescu-Mizil · Guillermo Cecchi · Aurelie Lozano
• 2010 Placeholder: Opening Remarks »
  Terrence Sejnowski · Neil D Lawrence
• 2010 Session: Spotlights Session 12 »
  Irina Rish
• 2010 Session: Oral Session 15 »
  Irina Rish
• 2010 Poster: Brain covariance selection: better individual functional connectivity models using population prior »
  Gaël Varoquaux · Alexandre Gramfort · Jean-Baptiste Poline · Bertrand Thirion
• 2010 Oral: A rational decision making framework for inhibitory control »
  Pradeep Shenoy · Rajesh PN Rao · Angela Yu
• 2010 Poster: Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models »
  Felipe Gerhard · Wulfram Gerstner
• 2010 Poster: Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics »
  Kanaka Rajan · L F Abbott · Haim Sompolinsky
• 2010 Poster: A rational decision making framework for inhibitory control »
  Pradeep Shenoy · Rajesh PN Rao · Angela Yu
• 2010 Talk: Opening Remarks and Awards »
  Richard Zemel · Terrence Sejnowski · John Shawe-Taylor
• 2010 Poster: Short-term memory in neuronal networks through dynamical compressed sensing »
  Surya Ganguli · Haim Sompolinsky
• 2009 Workshop: Manifolds, sparsity, and structured models: When can low-dimensional geometry really help? »
  Richard Baraniuk · Volkan Cevher · Mark A Davenport · Piotr Indyk · Bruno Olshausen · Michael B Wakin
• 2009 Workshop: The Curse of Dimensionality Problem: How Can the Brain Solve It? »
  Simon Haykin · Terrence Sejnowski · Steven W Zucker
• 2009 Poster: Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning »
  Robert Legenstein · Steven Chase · Andrew B Schwartz · Wolfgang Maass
• 2009 Oral: Functional Network Reorganization In Motor Cortex Can Be Explained by Reward-Modulated Hebbian Learning »
  Robert Legenstein · Steven Chase · Andrew B Schwartz · Wolfgang Maass
• 2009 Poster: Discriminative Network Models of Schizophrenia »
  Guillermo Cecchi · Irina Rish · Benjamin Thyreau · Bertrand Thirion · Marion Plaze · Jean-Luc Martinot · Marie Laure Paillere-Martinot · Jean-Baptiste Poline
• 2009 Poster: STDP enables spiking neurons to detect hidden causes of their inputs »
  Bernhard Nessler · Michael Pfeiffer · Wolfgang Maass
• 2009 Spotlight: STDP enables spiking neurons to detect hidden causes of their inputs »
  Bernhard Nessler · Michael Pfeiffer · Wolfgang Maass
• 2009 Oral: Discriminative Network Models of Schizophrenia »
  Guillermo Cecchi · Irina Rish · Benjamin Thyreau · Bertrand Thirion · Marion Plaze · Jean-Luc Martinot · Marie Laure Paillere-Martinot · Jean-Baptiste Poline
• 2009 Poster: Bayesian estimation of orientation preference maps »
  Jakob H Macke · Sebastian Gerwinn · Leonard White · Matthias Kaschube · Matthias Bethge
• 2009 Poster: Code-specific policy gradient rules for spiking neurons »
  Henning Sprekeler · Guillaume Hennequin · Wulfram Gerstner
• 2009 Poster: Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks »
  Stefan Klampfl · Wolfgang Maass
• 2009 Spotlight: Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks »
  Stefan Klampfl · Wolfgang Maass
• 2008 Workshop: New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis »
  Melissa K Carroll · Irina Rish · Francisco Pereira · Guillermo Cecchi
• 2008 Workshop: Cortical Microcircuits and their Computational Functions »
  Tomaso Poggio · Terrence Sejnowski
• 2008 Poster: Sparse Signal Recovery Using Markov Random Fields »
  Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk
• 2008 Spotlight: Sparse Signal Recovery Using Markov Random Fields »
  Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk
• 2008 Poster: Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning »
  Gediminas Luksys · Carmen Sandi · Wulfram Gerstner
• 2008 Oral: Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning »
  Gediminas Luksys · Carmen Sandi · Wulfram Gerstner
• 2008 Poster: Hebbian Learning of Bayes Optimal Decisions »
  Bernhard Nessler · Michael Pfeiffer · Wolfgang Maass
• 2007 Oral: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
  Sebastian Gerwinn · Jakob H Macke · Matthias Seeger · Matthias Bethge
• 2007 Spotlight: Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity »
  Robert Legenstein · Dejan Pecevski · Wolfgang Maass
• 2007 Poster: Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity »
  Robert Legenstein · Dejan Pecevski · Wolfgang Maass
• 2007 Poster: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
  Sebastian Gerwinn · Jakob H Macke · Matthias Seeger · Matthias Bethge
• 2007 Poster: An online Hebbian learning rule that performs Independent Component Analysis »
  Claudia Clopath · André Longtin · Wulfram Gerstner
• 2007 Poster: Receptive Fields without Spike-Triggering »
  Jakob H Macke · Günther Zeck · Matthias Bethge
• 2007 Poster: Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons »
  Lars Buesing · Wolfgang Maass
• 2007 Poster: Random Projections for Manifold Learning »
  Chinmay Hegde · Richard Baraniuk
• 2006 Workshop: Novel Applications of Dimensionality Reduction »
  John Blitzer · Rajarshi Das · Irina Rish · Kilian Q Weinberger
• 2006 Workshop: Echo State Networks and Liquid State Machines »
  Herbert Jaeger · Wolfgang Maass · Jose C Principe
• 2006 Workshop: Decoding the neural code »
  Eric Thomson · Bill Kristan · Terrence Sejnowski
• 2006 Poster: Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning »
  Gediminas Luksys · Jeremie Knuesel · Denis Sheynikhovich · Carmen Sandi · Wulfram Gerstner
• 2006 Poster: Temporal dynamics of information content carried by neurons in the primary visual cortex »
  Danko Nikolic · Stefan Haeusler · Wolf Singer · Wolfgang Maass
• 2006 Poster: Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons »
  Stefan Klampfl · Robert Legenstein · Wolfgang Maass
• 2006 Poster: Inducing Metric Violations in Human Similarity Judgements »
  Julian Laub · Jakob H Macke · Klaus-Robert Müller · Felix A Wichmann
• 2006 Poster: Learning Nonparametric Models for Probabilistic Imitation »
  David Grimes · Daniel Rashid · Rajesh PN Rao