Deep learning with differential Gaussian process flows

Deep learning with differential Gaussian process flows

9 October 2018

Pashupati Hegde

Markus Heinonen

Harri Lähdesmäki

Papers citing "Deep learning with differential Gaussian process flows"

10 / 10 papers shown

Title
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 33 93 0 02 Nov 2021
Approximate Latent Force Model Inference Jacob Moss Felix L. Opolka Bianca Dumitrascu Pietro Lio 49 3 0 24 Sep 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 27 46 0 12 Feb 2021
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 58 0 08 Nov 2020
Probabilistic Numeric Convolutional Neural Networks Marc Finzi Roberto Bondesan Max Welling BDL AI4TS 29 13 0 21 Oct 2020
Learning Continuous-Time Dynamics by Stochastic Differential Networks Yingru Liu Yucheng Xing Xuewen Yang Xin Wang Jing Shi Di Jin Zhaoyue Chen BDL 26 6 0 11 Jun 2020
All your loss are belong to Bayes Christian J. Walder Richard Nock 16 5 0 08 Jun 2020
Scalable Gradients for Stochastic Differential Equations Xuechen Li Ting-Kam Leonard Wong Ricky T. Q. Chen David Duvenaud 17 310 0 05 Jan 2020
Bayesian Learning-Based Adaptive Control for Safety Critical Systems David D. Fan Jennifer Nguyen Rohan Thakker Nikhilesh Alatur Ali-akbar Agha-mohammadi Evangelos A. Theodorou BDL 34 84 0 05 Oct 2019
Posterior Inference for Sparse Hierarchical Non-stationary Models K. Monterrubio-Gómez L. Roininen S. Wade Theo Damoulas Mark Girolami 27 27 0 04 Apr 2018