More from the Same Authors

• 2021 : Learning the solar latent space: sigma-variational autoencoders for multiple channel solar imaging »
  Edward Brown · Christopher Bridges · Bernard Benson · Atilim Gunes Baydin
• 2021 : Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles »
  Bernard Benson · Christopher Bridges · Atilim Gunes Baydin
• 2021 : Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation »
  Stefano Bonasera · Giacomo Acciarini · Jorge Pérez-Hernández · Bernard Benson · Edward Brown · Eric Sutton · Moriba Jah · Christopher Bridges · Atilim Gunes Baydin
• 2022 : Inferring molecular complexity from mass spectrometry data using machine learning »
  Timothy Gebhard · Aaron C. Bell · Jian Gong · Jaden J. A. Hastings · George Fricke · Nathalie Cabrol · Scott Sandford · Michael Phillips · Kimberley Warren-Rhodes · Atilim Gunes Baydin
• 2023 Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences »
  Brian Nord · Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Siddharth Mishra-Sharma · Benjamin Nachman · Kyle Cranmer · Gilles Louppe · Savannah Thais
• 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
• 2021 : Session 3 | Contributed talk: Maximilian Dax, "Amortized Bayesian inference of gravitational waves with normalizing flows" »
  Maximilian Dax · Atilim Gunes Baydin
• 2021 : Session 3 | Invited talk: Laure Zanna, "The future of climate modeling in the age of machine learning" »
  Laure Zanna · Atilim Gunes Baydin
• 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 : Session 2 | Contributed talk: George Stein, "Self-supervised similarity search for large scientific datasets" »
  George Stein · Atilim Gunes Baydin
• 2021 : Session 2 | Invited talk: Megan Ansdell, "NASA's efforts & opportunities to support ML in the Physical Sciences" »
  Megan Ansdell · Atilim Gunes Baydin
• 2021 : Session 1 | Contributed talk: Tian Xie, "Crystal Diffusion Variational Autoencoder for Periodic Material Generation" »
  Tian Xie · Atilim Gunes Baydin
• 2021 : Session 1 | Invited talk: Bingqing Cheng, "Predicting material properties with the help of machine learning" »
  Bingqing Cheng · Atilim Gunes Baydin
• 2021 : Session 1 | Invited talk: Max Welling, "Accelerating simulations of nature, both classical and quantum, with equivariant deep learning" »
  Max Welling · Atilim Gunes Baydin
• 2021 : Parallel-Friendly Automatic Differentiation in Dex and JAX »
  Adam Paszke
• 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: Domain Invariant Representation Learning with Domain Density Transformations »
  A. Tuan Nguyen · Toan Tran · Yarin Gal · Atilim Gunes Baydin
• 2020 : Session 3 | Invited talk: Laura Waller, "Physics-based Learning for Computational Microscopy" »
  Laura Waller · Atilim Gunes Baydin
• 2020 : Session 2 | Invited talk: Phiala Shanahan, "Generative Flow Models for Gauge Field Theory" »
  Phiala Shanahan · Atilim Gunes Baydin
• 2020 : Session 2 | Invited talk: Estelle Inack, "Variational Neural Annealing" »
  Estelle Inack · Atilim Gunes Baydin
• 2020 : Session 1 | Invited talk: Michael Bronstein, "Geometric Deep Learning for Functional Protein Design" »
  Michael Bronstein · Atilim Gunes Baydin
• 2020 : Session 1 | Invited talk: Lauren Anderson, "3D Milky Way Dust Map using a Scalable Gaussian Process" »
  Lauren Anderson · Atilim Gunes Baydin
• 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 Poster: Black-Box Optimization with Local Generative Surrogates »
  Sergey Shirobokov · Vladislav Belavin · Michael Kagan · Andrei Ustyuzhanin · Atilim Gunes Baydin
• 2019 : Towards Polyhedral Automatic Differentiation »
  Jan Hueckelheim
• 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: Program Transformations for ML »
  Pascal Lamblin · Atilim Gunes Baydin · Alexander Wiltschko · Bart van Merriënboer · Emily Fertig · Barak Pearlmutter · David Duvenaud · Laurent Hascoet
• 2019 Poster: PyTorch: An Imperative Style, High-Performance Deep Learning Library »
  Adam Paszke · Sam Gross · Francisco Massa · Adam Lerer · James Bradbury · Gregory Chanan · Trevor Killeen · Zeming Lin · Natalia Gimelshein · Luca Antiga · Alban Desmaison · Andreas Kopf · Edward Yang · Zachary DeVito · Martin Raison · Alykhan Tejani · Sasank Chilamkurthy · Benoit Steiner · Lu Fang · Junjie Bai · Soumith Chintala
• 2019 Poster: Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model »
  Atilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood
• 2017 : Automatic Differentiation of Parallelised Convolutional Neural Networks - Lessons from Adjoint PDE Solvers »
  Jan Hueckelheim
• 2017 : Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation »
  Jeffrey Siskind
• 2017 : Some highlights on Source-to-Source Adjoint AD »
  Laurent Hascoet
• 2017 : Algorithmic differentiation techniques in the deep learning context »
  Jean Utke
• 2017 : Optimal Smoothing for Pathwise Adjoints »
  Jonathan Hüser
• 2017 : Automatic differentiation in PyTorch »
  Adam Paszke
• 2017 : Beyond backprop: automatic differentiation in machine learning »
  Atilim Gunes Baydin
• 2017 Workshop: Deep Learning for Physical Sciences »
  Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood
• 2016 : Jeffrey M. Siskind – The tension between convenience and performance in automatic differentiation »
  Jeffrey Siskind
• 2011 Poster: Nonstandard Interpretations of Probabilistic Programs for Efficient Inference »
  David Wingate · Noah Goodman · Andreas Stuhlmueller · Jeffrey Siskind