automatic differentiation

A Simple and Efficient Tensor Calculus AAAI 2020 Automatic Differentiation of Sketched Regression AISTATS 2020 Automatic Differentiation of Some First-Order Methods in Parametric Optimization AISTATS 2020 BackPACK: Packing more into Backprop ICLR 2020 Deep Differentiable Grasp Planner for High-DOF Grippers RSS 2020 DiffTaichi: Differentiable Programming for Physical Simulation ICLR 2020 Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits ICML 2020 Implicit differentiation of Lasso-type models for hyperparameter optimization ICML 2020 Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients NeurIPS 2020 Learning Differential Equations that are Easy to Solve NeurIPS 2020 Neural Likelihoods via Cumulative Distribution Functions UAI 2020 On Correctness of Automatic Differentiation for Non-Differentiable Functions NeurIPS 2020 Super-efficiency of automatic differentiation for functions defined as a minimum ICML 2020 Tensor Train Decomposition on TensorFlow (T3F) JMLR 2020 A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization RSS 2019 Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era AISTATS 2019 Differentiable Convex Optimization Layers NeurIPS 2019 Online Normalization for Training Neural Networks NeurIPS 2019 PyTorch: An Imperative Style, High-Performance Deep Learning Library NeurIPS 2019 Reversible Jump Probabilistic Programming AISTATS 2019 Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning ICCV 2019 Analytical Derivatives of Rigid Body Dynamics Algorithms RSS 2018 Automatic Differentiation in Machine Learning: a Survey JMLR 2018 Automatic differentiation in ML: Where we are and where we should be going NeurIPS 2018 Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming NeurIPS 2018