Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations

12 February 2021

Papers citing "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

33 / 33 papers shown

Title
Uncertainty quantification of neural network models of evolving processes via Langevin sampling C. Safta Reese E. Jones Ravi G. Patel Raelynn Wonnacot Dan S. Bolintineanu Craig M. Hamel S. Kramer BDL 31 0 0 21 Apr 2025
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations Linus Heck Maximilian Gelbrecht Michael T. Schaub Niklas Boers DiffM 28 0 0 23 Dec 2024
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines Jesus Garcia Fernandez Nasir Ahmad Marcel van Gerven 27 1 0 17 Oct 2024
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations Richard Bergna Sergio Calvo-Ordoñez Felix L. Opolka Pietro Liò Jose Miguel Hernandez-Lobato BDL 39 2 0 28 Aug 2024
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations Jian Xu Zhiqi Lin Min Chen Junmei Yang Delu Zeng John Paisley 19 0 0 12 Aug 2024
Partially Stochastic Infinitely Deep Bayesian Neural Networks Sergio Calvo-Ordoñez Matthieu Meunier Francesco Piatti Yuantao Shi BDL 37 3 0 05 Feb 2024
A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems Cheng Yin Yi Chen 13 0 0 07 Nov 2023
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation Konstantin Hess Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 27 14 0 26 Oct 2023
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations Hardeep Bassi Yuanran Zhu Senwei Liang Jia Yin Cian C. Reeves Vojtěch Vlček Chao Yang 29 8 0 13 Oct 2023
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 26 40 0 04 Oct 2023
Graph Neural Stochastic Differential Equations Richard Bergna Felix L. Opolka Pietro Lio' Jose Miguel Hernandez-Lobato 16 2 0 23 Aug 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature Alexander Norcliffe M. Deisenroth 23 3 0 21 Aug 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions Francisco Vargas Shreyas Padhy Denis Blessing Nikolas Nusken DiffM OT 40 3 0 03 Jul 2023
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations Franck Djeumou Cyrus Neary Ufuk Topcu DiffM 24 8 0 10 Jun 2023
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations Anudhyan Boral Z. Y. Wan Leonardo Zepeda-Núnez James Lottes Qing Wang Yi-fan Chen John R. Anderson Fei Sha AI4CE PINN 16 11 0 01 Jun 2023
Thermodynamic AI and the fluctuation frontier Patrick J. Coles Collin Szczepanski Denis Melanson Kaelan Donatella Antonio J. Martinez Faris M. Sbahi AI4CE 32 16 0 09 Feb 2023
Changes from Classical Statistics to Modern Statistics and Data Science Kai Zhang Shan-Yu Liu M. Xiong 26 0 0 30 Oct 2022
Variational Inference for Infinitely Deep Neural Networks Achille Nazaret David M. Blei BDL 23 11 0 21 Sep 2022
Continuous-time Particle Filtering for Latent Stochastic Differential Equations Ruizhi Deng Greg Mori Andreas M. Lehrmann BDL 20 0 0 01 Sep 2022
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics Zhichao Li T. Duong Nikolay A. Atanasov 11 1 0 22 Jul 2022
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Martin Gonzalez H. Hajri Loic Cantat M. Petreczky 27 1 0 16 Jun 2022
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings Martin Gonzalez Thibault Defourneau H. Hajri M. Petreczky 31 2 0 24 May 2022
Learning Stochastic Dynamics with Statistics-Informed Neural Network Yuanran Zhu Yunhao Tang Changho Kim 19 18 0 24 Feb 2022
Predicting the impact of treatments over time with uncertainty aware neural differential equations E. Brouwer J. Hernández Stephanie L. Hyland OOD CML 8 25 0 24 Feb 2022
Characteristic Neural Ordinary Differential Equations Xingzi Xu Ali Hasan Khalil Elkhalil Jie Ding Vahid Tarokh BDL 19 3 0 25 Nov 2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows Francisco Vargas Andrius Ovsianas David Fernandes Mark Girolami Neil D. Lawrence Nikolas Nusken BDL 32 45 0 20 Nov 2021
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 22 93 0 02 Nov 2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models Sophie Gruenbacher Mathias Lechner Ramin Hasani Daniela Rus T. Henzinger S. Smolka Radu Grosu 18 17 0 18 Jul 2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models Lenart Treven Philippe Wenk Florian Dorfler Andreas Krause OOD 11 2 0 22 Jun 2021
Stateful ODE-Nets using Basis Function Expansions A. Queiruga N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 27 16 0 21 Jun 2021
A Variational Perspective on Diffusion-Based Generative Models and Score Matching Chin-Wei Huang Jae Hyun Lim Aaron Courville DiffM 30 186 0 05 Jun 2021
Efficient and Accurate Gradients for Neural SDEs Patrick Kidger James Foster Xuechen Li Terry Lyons DiffM 19 60 0 27 May 2021
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates Lingkai Kong Jimeng Sun Chao Zhang UQCV 42 103 0 24 Aug 2020