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Author Information
Jacob Andreas (MIT)
Edward Gibson (MIT)
Stefan Lee (Oregon State University)
Noga Zaslavsky (MIT)
Jason Eisner (Johns Hopkins University + Microsoft Semantic Machines)
Jason Eisner is Professor of Computer Science at Johns Hopkins University, as well as Director of Research at Microsoft Semantic Machines. He is a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Machine Learning Group, the Cognitive Science Department, and the national Center of Excellence in Human Language Technology. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. His 135+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; and unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation. He is also the lead designer of Dyna, a new declarative programming language that provides an infrastructure for AI research. He has received two school-wide awards for excellence in teaching, as well as recent Best Paper Awards at ACL 2017 and EMNLP 2019.
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)
Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through users of the world's most valuable public companies, e.g., for greatly improved (CTC-LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTM-based translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of mathematically rigorous universal AI and recursive self-improvement in metalearning machines that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game to achieve artificial curiosity (GANs are a special case). In 1991, he introduced very deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what's now called linear Transformers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and advising various governments on AI strategies.
More from the Same Authors
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2021 : Learning Adaptive Control Flow in Transformers for Improved Systematic Generalization »
Róbert Csordás · Kazuki Irie · Jürgen Schmidhuber -
2021 : Augmenting Classic Algorithms with Neural Components for Strong Generalisation on Ambiguous and High-Dimensional Data »
Imanol Schlag · Jürgen Schmidhuber -
2021 : Improving Baselines in the Wild »
Kazuki Irie · Imanol Schlag · Róbert Csordás · Jürgen Schmidhuber -
2021 : A Modern Self-Referential Weight Matrix That Learns to Modify Itself »
Kazuki Irie · Imanol Schlag · Róbert Csordás · Jürgen Schmidhuber -
2021 : Exploring through Random Curiosity with General Value Functions »
Aditya Ramesh · Louis Kirsch · Sjoerd van Steenkiste · Jürgen Schmidhuber -
2021 : Unsupervised Learning of Temporal Abstractions using Slot-based Transformers »
Anand Gopalakrishnan · Kazuki Irie · Jürgen Schmidhuber · Sjoerd van Steenkiste -
2021 : Unsupervised Learning of Temporal Abstractions using Slot-based Transformers »
Anand Gopalakrishnan · Kazuki Irie · Jürgen Schmidhuber · Sjoerd van Steenkiste -
2022 : Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks »
Kazuki Irie · Jürgen Schmidhuber -
2022 : On Narrative Information and the Distillation of Stories »
Dylan Ashley · Vincent Herrmann · Zachary Friggstad · Jürgen Schmidhuber -
2022 : The Benefits of Model-Based Generalization in Reinforcement Learning »
Kenny Young · Aditya Ramesh · Louis Kirsch · Jürgen Schmidhuber -
2023 Poster: Contrastive Training of Complex-Valued Autoencoders for Object Discovery »
Aleksandar Stanić · Anand Gopalakrishnan · Kazuki Irie · Jürgen Schmidhuber -
2023 Poster: The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks »
Ziqian Zhong · Ziming Liu · Max Tegmark · Jacob Andreas -
2023 Poster: A Function Interpretation Benchmark for Evaluating Interpretability Methods »
Sarah Schwettmann · Tamar Shaham · Joanna Materzynska · Neil Chowdhury · Shuang Li · Jacob Andreas · David Bau · Antonio Torralba -
2023 Poster: BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing »
Subhro Roy · Samuel Thomson · Tongfei Chen · Richard Shin · Adam Pauls · Jason Eisner · Benjamin Van Durme -
2023 Oral: The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks »
Ziqian Zhong · Ziming Liu · Max Tegmark · Jacob Andreas -
2022 : Learning gaze control, external attention, and internal attention since 1990-91 »
Jürgen Schmidhuber -
2022 Workshop: LaReL: Language and Reinforcement Learning »
Laetitia Teodorescu · Laura Ruis · Tristan Karch · Cédric Colas · Paul Barde · Jelena Luketina · Athul Jacob · Pratyusha Sharma · Edward Grefenstette · Jacob Andreas · Marc-Alexandre Côté -
2022 Poster: Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules »
Kazuki Irie · Francesco Faccio · Jürgen Schmidhuber -
2022 Poster: Exploring through Random Curiosity with General Value Functions »
Aditya Ramesh · Louis Kirsch · Sjoerd van Steenkiste · Jürgen Schmidhuber -
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 -
2021 : Q/A Session »
Alice Xiang · Jacob Andreas -
2021 : [IT5] Natural language descriptions of deep features »
Jacob Andreas -
2021 : Panel Discussion 1 »
Megan Peters · Jürgen Schmidhuber · Simona Ghetti · Nick Roy · Oiwi Parker Jones · Ingmar Posner -
2021 : Credit Assignment & Meta-Learning in a Single Lifelong Trial »
Jürgen Schmidhuber -
2021 Poster: Going Beyond Linear Transformers with Recurrent Fast Weight Programmers »
Kazuki Irie · Imanol Schlag · Róbert Csordás · Jürgen Schmidhuber -
2021 Poster: Meta Learning Backpropagation And Improving It »
Louis Kirsch · Jürgen Schmidhuber -
2021 Poster: Teachable Reinforcement Learning via Advice Distillation »
Olivia Watkins · Abhishek Gupta · Trevor Darrell · Pieter Abbeel · Jacob Andreas -
2020 Poster: Noise-Contrastive Estimation for Multivariate Point Processes »
Hongyuan Mei · Tom Wan · Jason Eisner -
2020 Poster: A Benchmark for Systematic Generalization in Grounded Language Understanding »
Laura Ruis · Jacob Andreas · Marco Baroni · Diane Bouchacourt · Brenden Lake -
2020 Poster: Compositional Explanations of Neurons »
Jesse Mu · Jacob Andreas -
2020 Oral: Compositional Explanations of Neurons »
Jesse Mu · Jacob Andreas -
2019 : Invited Talk - 5 »
Stefan Lee -
2019 : Invited Talk - 4 »
Jacob Andreas -
2019 : Invited Talk - 3 »
Jason Eisner -
2019 : Invited Talk - 2 »
Noga Zaslavsky -
2019 : Invited Talk - 1 »
Edward Gibson -
2019 : Opening Remarks »
Florian Strub · Harm de Vries · Abhishek Das · Stefan Lee · Erik Wijmans · Dor Arad Hudson · Alane Suhr -
2019 Workshop: Visually Grounded Interaction and Language »
Florian Strub · Abhishek Das · Erik Wijmans · Harm de Vries · Stefan Lee · Alane Suhr · Dor Arad Hudson -
2019 Poster: Are Disentangled Representations Helpful for Abstract Visual Reasoning? »
Sjoerd van Steenkiste · Francesco Locatello · Jürgen Schmidhuber · Olivier Bachem -
2019 Poster: ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks »
Jiasen Lu · Dhruv Batra · Devi Parikh · Stefan Lee -
2019 Poster: Chasing Ghosts: Instruction Following as Bayesian State Tracking »
Peter Anderson · Ayush Shrivastava · Devi Parikh · Dhruv Batra · Stefan Lee -
2018 : Panel Discussion »
Rich Caruana · Mike Schuster · Ralf Schlüter · Hynek Hermansky · Renato De Mori · Samy Bengio · Michiel Bacchiani · Jason Eisner -
2018 : Jason Eisner, "BiLSTM-FSTs and Neural FSTs" »
Jason Eisner -
2018 : Invited Speaker #4 Juergen Schmidhuber »
Jürgen Schmidhuber -
2018 Poster: Recurrent World Models Facilitate Policy Evolution »
David Ha · Jürgen Schmidhuber -
2018 Oral: Recurrent World Models Facilitate Policy Evolution »
David Ha · Jürgen Schmidhuber -
2018 Poster: Overcoming Language Priors in Visual Question Answering with Adversarial Regularization »
Sainandan Ramakrishnan · Aishwarya Agrawal · Stefan Lee -
2018 Poster: Speaker-Follower Models for Vision-and-Language Navigation »
Daniel Fried · Ronghang Hu · Volkan Cirik · Anna Rohrbach · Jacob Andreas · Louis-Philippe Morency · Taylor Berg-Kirkpatrick · Kate Saenko · Dan Klein · Trevor Darrell -
2018 Poster: Learning to Reason with Third Order Tensor Products »
Imanol Schlag · Jürgen Schmidhuber -
2017 : Afternoon Panel discussion »
Brian Skyrms · Satinder Singh · Jacob Andreas -
2017 : Efficient human-like semantic representations via the information bottleneck principle »
Noga Zaslavsky -
2017 : Morning panel discussion »
Jürgen Schmidhuber · Noah Goodman · Anca Dragan · Pushmeet Kohli · Dhruv Batra -
2017 : Poster session (and Coffee Break) »
Jacob Andreas · Kun Li · Conner Vercellino · Thomas Miconi · Wenpeng Zhang · Luca Franceschi · Zheng Xiong · Karim Ahmed · Laurent Itti · Tim Klinger · Mostafa Rohaninejad -
2017 : HRL with gradient-based subgoal generators, asymptotically optimal incremental problem solvers, various meta-learners, and PowerPlay (Jürgen Schmidhuber) »
Jürgen Schmidhuber -
2017 : Invited Talk »
Jürgen Schmidhuber -
2017 Poster: Neural Expectation Maximization »
Klaus Greff · Sjoerd van Steenkiste · Jürgen Schmidhuber -
2017 Poster: The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process »
Hongyuan Mei · Jason Eisner -
2016 : Juergen Schmidhuber (Scientific Director of the Swiss AI Lab IDSIA) »
Jürgen Schmidhuber -
2016 Symposium: Recurrent Neural Networks and Other Machines that Learn Algorithms »
Jürgen Schmidhuber · Sepp Hochreiter · Alex Graves · Rupesh K Srivastava -
2016 Poster: Tagger: Deep Unsupervised Perceptual Grouping »
Klaus Greff · Antti Rasmus · Mathias Berglund · Hotloo Xiranood · Harri Valpola · Jürgen Schmidhuber -
2015 : Deep Learning RNNaissance »
Jürgen Schmidhuber -
2015 : On General Problem Solving and How to Learn an Algorithm »
Jürgen Schmidhuber -
2015 Poster: Training Very Deep Networks »
Rupesh K Srivastava · Klaus Greff · Jürgen Schmidhuber -
2015 Spotlight: Training Very Deep Networks »
Rupesh K Srivastava · Klaus Greff · Jürgen Schmidhuber -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
2015 Poster: Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation »
Marijn F Stollenga · Wonmin Byeon · Marcus Liwicki · Jürgen Schmidhuber -
2014 Poster: Unsupervised Transcription of Piano Music »
Taylor Berg-Kirkpatrick · Jacob Andreas · Dan Klein -
2014 Poster: Learning to Search in Branch and Bound Algorithms »
He He · Hal Daumé III · Jason Eisner -
2014 Spotlight: Unsupervised Transcription of Piano Music »
Taylor Berg-Kirkpatrick · Jacob Andreas · Dan Klein -
2014 Poster: Deep Networks with Internal Selective Attention through Feedback Connections »
Marijn F Stollenga · Jonathan Masci · Faustino Gomez · Jürgen Schmidhuber -
2013 Poster: Compete to Compute »
Rupesh K Srivastava · Jonathan Masci · Sohrob Kazerounian · Faustino Gomez · Jürgen Schmidhuber -
2012 Poster: Imitation Learning by Coaching »
He He · Hal Daumé III · Jason Eisner -
2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
Jiarong Jiang · Adam Teichert · Hal Daumé III · Jason Eisner -
2012 Poster: Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images »
Dan Ciresan · Alessandro Giusti · luca Maria Gambardella · Jürgen Schmidhuber -
2010 Poster: Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices »
Yi Sun · Faustino Gomez · Jürgen Schmidhuber -
2008 Poster: Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks »
Alex Graves · Jürgen Schmidhuber -
2008 Spotlight: Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks »
Alex Graves · Jürgen Schmidhuber -
2007 Poster: Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks »
Alex Graves · Santiago Fernandez · Marcus Liwicki · Horst Bunke · Jürgen Schmidhuber