Monte Carlo Gradient Estimation in Machine Learning

25 June 2019

Papers citing "Monte Carlo Gradient Estimation in Machine Learning"

41 / 91 papers shown

Title
Gradients without Backpropagation A. G. Baydin Barak A. Pearlmutter Don Syme Frank D. Wood Philip H. S. Torr 32 66 0 17 Feb 2022
A Differential Entropy Estimator for Training Neural Networks Georg Pichler Pierre Colombo Malik Boudiaf Günther Koliander Pablo Piantanida 25 21 0 14 Feb 2022
Do Differentiable Simulators Give Better Policy Gradients? H. Suh Max Simchowitz Kaipeng Zhang Russ Tedrake 30 95 0 02 Feb 2022
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference Wyatt Bridgman Xiaoxuan Zhang G. Teichert M. Khalil K. Garikipati Reese E. Jones UQCV AI4CE 16 5 0 31 Jan 2022
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization I. Nikoloska Osvaldo Simeone 17 10 0 21 Jan 2022
$$m^\ast$ of two-dimensional electron gas: a neural canonical transformation study$ $m^\ast$ of two-dimensional electron gas: a neural canonical transformation study H.-j. Xie Linfeng Zhang Lei Wang 28 8 0 10 Jan 2022
Scaling Structured Inference with Randomization Yao Fu John P. Cunningham Mirella Lapata BDL 32 2 0 07 Dec 2021
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy Aditya Grover Stefano Ermon CML 40 72 0 06 Dec 2021
Gradients are Not All You Need Luke Metz C. Freeman S. Schoenholz Tal Kachman 28 93 0 10 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 20 10 0 08 Nov 2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods Desi R. Ivanova Adam Foster Steven Kleinegesse Michael U. Gutmann Tom Rainforth OffRL 13 46 0 03 Nov 2021
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning Iris A. M. Huijben W. Kool Max B. Paulus Ruud J. G. van Sloun 26 93 0 04 Oct 2021
Stochastic Contrastive Learning Jason Ramapuram Dan Busbridge Xavier Suau Russ Webb BDL SSL 44 3 0 01 Oct 2021
Autoregressive neural-network wavefunctions for ab initio quantum chemistry Thomas D. Barrett A. Malyshev A. Lvovsky 25 69 0 26 Sep 2021
A principled stopping rule for importance sampling Medha Agarwal Dootika Vats Victor Elvira 33 2 0 30 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 43 40 0 09 Aug 2021
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 23 12 0 02 Aug 2021
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation Xiaohui Chen Xu Han Jiajing Hu Francisco J. R. Ruiz Liping Liu BDL 24 34 0 11 Jun 2021
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds Steven Kleinegesse Michael U. Gutmann FedML 30 25 0 10 May 2021
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile van Krieken Jakub M. Tomczak A. T. Teije ODL OffRL 28 15 0 01 Apr 2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning Sebastian Curi Ilija Bogunovic Andreas Krause 39 17 0 18 Mar 2021
Continuous normalizing flows on manifolds Luca Falorsi BDL AI4CE 27 10 0 14 Mar 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster Desi R. Ivanova Ilyas Malik Tom Rainforth 28 78 0 03 Mar 2021
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs Thomas Spooner N. Vadori Sumitra Ganesh 22 7 0 20 Feb 2021
Population synthesis for urban resident modeling using deep generative models M. Johnsen Oliver Brandt Sergio Garrido Francisco Câmara Pereira 24 11 0 13 Nov 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 136 48 0 20 Oct 2020
A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games Shiva Navabi Osonde A. Osoba 8 2 0 04 Oct 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi Felix Berkenkamp Andreas Krause 25 82 0 15 Jun 2020
Gradient Estimation with Stochastic Softmax Tricks Max B. Paulus Dami Choi Daniel Tarlow Andreas Krause Chris J. Maddison BDL 36 85 0 15 Jun 2020
Model-Augmented Actor-Critic: Backpropagating through Paths I. Clavera Yao Fu Pieter Abbeel 33 86 0 16 May 2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models Théo Galy-Fajou F. Wenzel Manfred Opper 16 4 0 26 Feb 2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation Steven Kleinegesse Michael U. Gutmann 18 64 0 19 Feb 2020
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels Sangwoo Park Osvaldo Simeone Joonhyuk Kang 19 51 0 22 Oct 2019
Learning Generalisable Omni-Scale Representations for Person Re-Identification Kaiyang Zhou Yongxin Yang Andrea Cavallaro Tao Xiang 30 217 0 15 Oct 2019
The Differentiable Cross-Entropy Method Brandon Amos Denis Yarats 26 54 0 27 Sep 2019
Intensity-Free Learning of Temporal Point Processes Oleksandr Shchur Marin Bilos Stephan Günnemann AI4TS 19 167 0 26 Sep 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 35 185 0 05 Jun 2019
Improving Discrete Latent Representations With Differentiable Approximation Bridges Jason Ramapuram Russ Webb DRL 11 9 0 09 May 2019
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles Xiaowei Hu A. PrashanthL. András Gyorgy Csaba Szepesvári 78 65 0 22 Sep 2016
Stochastic Backpropagation through Mixture Density Distributions Alex Graves BDL 51 44 0 19 Jul 2016