ICLRW 2020

40 papers

A Free-Energy Principle for Representation Learning Yansong Gao, Pratik Chaudhari

A Mean-Field Analysis of Deep ResNet and Beyond:Towards Provable Optimization via Overparameterization from Depth Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying

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Amortized Finite Element Analysis for Fast PDE-Constrained Optimization Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams

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Bringing PDEs to JAX with Forward and Reverse Modes Automatic Differentiation Ivan Yashchuk

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Can Auto-Encoders Help with Filling Missing Data? Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mário A. T. Figueiredo

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Comparing Recurrent and Convolutional Neural Networks for Predicting Wave Propagation Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia, Chris Cantwell, Amos Storkey, Anil A. Bharath

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Constrained Neural Ordinary Differential Equations with Stability Guarantees Aaron Tuor, Jan Drgona, Draguna Vrabie

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Deep Ritz Revisited Johannes Müller, Marius Zeinhofer

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Differentiable Molecular Simulations for Control and Learning Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli

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Differentiable Physics Simulation Junbang Liang, Ming C. Lin

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Differential Equations as a Model Prior for Deep Learning and Its Applications in Robotics Michael Lutter, Jan Peters

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Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

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Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu, Michael Chertkov

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Encoder-Decoder Neural Network for Solving the Nonlinear Fokker-Planck-Landau Collision Operator in XGC Marco Andres Miller, Randy Michael Churchill, Choong-Seock Chang, Robert Hager

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Enforcing Physical Constraints in CNNs Through Differentiable PDE Layer Chiyu "Max" Jiang, Karthik Kashinath, Prabhat, Philip Marcus

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Fast Convergence for Langevin with Matrix Manifold Structure Ankur Moitra, Andrej Risteski

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Generating Control Policies for Autonomous Vehicles Using Neural ODEs Houston Lucas, Richard Kelley

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Generative ODE Modeling with Known Unknowns Ori Linial, Uri Shalit

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How Chaotic Are Recurrent Neural Networks? Pourya Vakilipourtakalou, Lili Mou

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Lagrangian Neural Networks Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, Shirley Ho

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Learning to Solve Differential Equations Across Initial Conditions Shehryar Malik, Usman Anwar, Ali Ahmed, Alireza Aghasi

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Learning-Based Strong Solutions to Forward and Inverse Problems in PDEs Leah Bar, Nir Sochen

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Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics Thanh V. Nguyen, Youssef Mroueh, Samuel Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde

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Neural Differential Equations for Single Image Super-Resolution Teven Le Scao

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Neural Dynamical Systems Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

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Neural Operator: Graph Kernel Network for Partial Differential Equations Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, Andrew Stuart

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Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces Stefano Massaroli, Michael Poli, Sanzhar Bakhtiyarov, Atsushi Yamashita, Hajime Asama, Jinkyoo Park

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Nonlinear Differential Equations with External Forcing Paul Pukite

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On the Space-Time Expressivity of ResNets Johannes Müller

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Port-Hamiltonian Gradient Flows Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park

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Progressive Growing of Neural ODEs Hammad A. Ayyubi, Yi Yao, Ajay Divakaran

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Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models Chin-Wei Huang, Laurent Dinh, Aaron Courville

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Stochastic Gradient Algorithms from ODE Splitting Perspective Daniil Merkulov, Ivan Oseledets

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Stochasticity in Neural ODEs: An Empirical Study Alexandra Volokhova, Viktor Oganesyan, Dmitry Vetrov

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The Equivalence Between Stein Variational Gradient Descent and Black-Box Variational Inference Casey Chu, Kentaro Minami, Kenji Fukumizu

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Time Dependence in Non-Autonomous Neural ODEs Jared Quincy Davis, Krzysztof Choromanski, Vikas Sindhwani, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia

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Towards Understanding Normalization in Neural ODEs Julia Gusak, Larisa Markeeva, Talgat Daulbaev, Alexander Katrutsa, Andrzej Cichocki, Ivan Oseledets

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Understanding and Improving Transformer from a Multi-Particle Dynamic System Point of View. Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-yan Liu

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Urban Air Pollution Forecasts Generated from Latent Space Representation Cesar Quilodran Casas, Rossella Arcucci, Yike Guo

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Wavelet-Powered Neural Networks for Turbulence Arvind T. Mohan, Daniel Livescu, Michael Chertkov

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