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A Deterministic Approximation to Neural SDEs

16 June 2020

Jan Peters

Papers citing "A Deterministic Approximation to Neural SDEs"

24 / 24 papers shown

Title
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning Abdullah Akgul Manuel Haußmann M. Kandemir OffRL 202 0 0 17 Jan 2025
Evidential Turing Processes M. Kandemir Abdullah Akgul Manuel Haussmann Gözde B. Ünal EDL UQCV BDL 64 10 0 02 Jun 2021
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 353 6,566 0 26 Nov 2020
Stochastic Differential Equations with Variational Wishart Diffusions Martin Jørgensen M. Deisenroth Hugh Salimbeni DiffM 41 8 0 26 Jun 2020
Calibrated Reliable Regression using Maximum Mean Discrepancy Peng Cui Wenbo Hu Jun Zhu UQCV 60 48 0 18 Jun 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties J. Lindinger David Reeb C. Lippert Barbara Rakitsch BDL UQCV 52 8 0 22 May 2020
Dream to Control: Learning Behaviors by Latent Imagination Danijar Hafner Timothy Lillicrap Jimmy Ba Mohammad Norouzi VLM 128 1,371 0 03 Dec 2019
Multiple Futures Prediction Yichuan Tang Ruslan Salakhutdinov 96 353 0 04 Nov 2019
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors Jayaraman J. Thiagarajan Bindya Venkatesh P. Sattigeri P. Bremer UQCV 56 31 0 09 Sep 2019
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model Alex X. Lee Anusha Nagabandi Pieter Abbeel Sergey Levine OffRL BDL 85 382 0 01 Jul 2019
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit Belinda Tzen Maxim Raginsky DiffM 166 210 0 23 May 2019
Augmented Neural ODEs Emilien Dupont Arnaud Doucet Yee Whye Teh BDL 152 632 0 02 Apr 2019
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs G. Abbati Philippe Wenk Michael A. Osborne Andreas Krause Bernhard Schölkopf Stefan Bauer DiffM 41 15 0 22 Feb 2019
Deep learning with differential Gaussian process flows Pashupati Hegde Markus Heinonen Harri Lähdesmäki Samuel Kaski BDL 63 42 0 09 Oct 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov Nathan Fenner Stefano Ermon BDL UQCV 201 636 0 01 Jul 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 434 5,157 0 19 Jun 2018
Bayesian Inference for Diffusion Processes: Using Higher-Order Approximations for Transition Densities Susanne Pieschner Christiane Fuchs 22 6 0 06 Jun 2018
Black-box Variational Inference for Stochastic Differential Equations Tom Ryder Andrew Golightly A. Mcgough D. Prangle 69 58 0 09 Feb 2018
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,862 0 14 Jun 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni M. Deisenroth BDL GP 91 422 0 24 May 2017
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 226 1,517 0 08 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 852 9,346 0 06 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 142 945 0 18 Feb 2015
Efficient Estimation of Mutual Information for Strongly Dependent Variables Shuyang Gao Greg Ver Steeg Aram Galstyan 87 199 0 07 Nov 2014