automatic differentiation

Separable Physics-Informed Neural Networks NeurIPS 2023 SynJax: Structured Probability Distributions for JAX EMNLP 2023 A Unified Framework for Implicit Sinkhorn Differentiation CVPR 2022 Automatic differentiation of nonsmooth iterative algorithms NeurIPS 2022 Automatic Differentiation of Programs with Discrete Randomness NeurIPS 2022 Convexity Certificates from Hessians NeurIPS 2022 Efficient and Modular Implicit Differentiation NeurIPS 2022 JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data JMLR 2022 Learning soft interventions in complex equilibrium systems UAI 2022 Optimization for Classical Machine Learning Problems on the GPU AAAI 2022 Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms NeurIPS 2022 Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning JMLR 2022 Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation ICML 2022 tntorch: Tensor Network Learning with PyTorch JMLR 2022 You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks PGM 2022 AUXILIARY TASK UPDATE DECOMPOSITION: THE GOOD, THE BAD AND THE NEUTRAL ICLR 2021 Differentiable Spline Approximations NeurIPS 2021 Kernel Operations on the GPU, with Autodiff, without Memory Overflows JMLR 2021 Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics ICML 2021 Randomized Automatic Differentiation ICLR 2021 RMP2: A Structured Composable Policy Class for Robot Learning RSS 2021 Storchastic: A Framework for General Stochastic Automatic Differentiation NeurIPS 2021 Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors CoRL 2021 Variational inference for nonlinear ordinary differential equations AISTATS 2021 A mathematical model for automatic differentiation in machine learning NeurIPS 2020