Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

23 May 2019

Papers citing "Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit"

50 / 64 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 41 0 0 21 Apr 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 57 0 0 16 Apr 2025
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning Anh Tong Thanh Nguyen-Tang Dongeun Lee Duc Nguyen Toan M. Tran David Hall Cheongwoong Kang Jaesik Choi 40 0 0 03 Mar 2025
Neural Guided Diffusion Bridges Gefan Yang Frank van der Meulen Stefan Sommer DiffM 65 0 0 17 Feb 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training Julius Berner Lorenz Richter Marcin Sendera Jarrid Rector-Brooks Nikolay Malkin OffRL 67 3 0 10 Jan 2025
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series Byoungwoo Park Hyungi Lee Juho Lee AI4TS 51 0 0 08 Oct 2024
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control Mengjian Hua Matthieu Laurière Eric Vanden-Eijnden 41 3 0 07 Oct 2024
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions Jianxin Zhang Josh Viktorov Doosan Jung Emily Pitler DiffM 51 0 0 04 Oct 2024
Adaptive teachers for amortized samplers Minsu Kim Sanghyeok Choi Taeyoung Yun Emmanuel Bengio Leo Feng Jarrid Rector-Brooks Sungsoo Ahn Jinkyoo Park Nikolay Malkin Yoshua Bengio 264 4 0 02 Oct 2024
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows Hyemin Gu Markos A. Katsoulakis Luc Rey-Bellet Benjamin J. Zhang 50 3 0 16 Jul 2024
ISR: Invertible Symbolic Regression Tony Tohme M. J. Khojasteh Mohsen Sadr Florian Meyer Kamal Youcef-Toumi 51 0 0 10 May 2024
Derivative-based regularization for regression Enrico Lopedoto Maksim Shekhunov Vitaly Aksenov K. Salako Tillman Weyde 34 0 0 01 May 2024
Variational Sampling of Temporal Trajectories Jurijs Nazarovs Zhichun Huang Xingjian Zhen Sourav Pal Rudrasis Chakraborty Vikas Singh 24 0 0 18 Mar 2024
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data YongKyung Oh Dongyoung Lim Sungil Kim AI4TS 47 13 0 22 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 45 42 0 09 Feb 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 71 18 0 07 Feb 2024
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent Kei Ishikawa BDL 63 0 0 03 Oct 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature Alexander Norcliffe M. Deisenroth 33 3 0 21 Aug 2023
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation Tony Tohme Mohsen Sadr K. Youcef-Toumi N. Hadjiconstantinou 35 3 0 07 Jun 2023
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations Anudhyan Boral Z. Y. Wan Leonardo Zepeda-Núñez James Lottes Qing Wang Yi-fan Chen John R. Anderson Fei Sha AI4CE PINN 37 11 0 01 Jun 2023
Non-adversarial training of Neural SDEs with signature kernel scores Zacharia Issa Blanka Horvath M. Lemercier C. Salvi AI4TS 48 24 0 25 May 2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look M. Kandemir Barbara Rakitsch Jan Peters BDL 38 6 0 02 May 2023
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks Hyeonjung Jung Jung Jayant Gupta B. Jayaprakash Matthew J. Eagon Harish Selvam Carl Molnar W. Northrop Shashi Shekhar AI4CE 45 5 0 26 Apr 2023
Variational Inference for Longitudinal Data Using Normalizing Flows Clément Chadebec S. Allassonnière BDL DRL 31 1 0 24 Mar 2023
Discouraging posterior collapse in hierarchical Variational Autoencoders using context Anna Kuzina Jakub M. Tomczak BDL DRL 28 1 0 20 Feb 2023
Using Intermediate Forward Iterates for Intermediate Generator Optimization Harshit Mishra Jurijs Nazarovs Manmohan Dogra Sathya Ravi DiffM 27 0 0 05 Feb 2023
Learning Data Representations with Joint Diffusion Models Kamil Deja Tomasz Trzciñski Jakub M. Tomczak DiffM 30 15 0 31 Jan 2023
Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent Richard Archibald F. Bao Yanzhao Cao Hui‐Jie Sun 57 2 0 17 Dec 2022
MAGVIT: Masked Generative Video Transformer Lijun Yu Yong Cheng Kihyuk Sohn José Lezama Han Zhang ... Alexander G. Hauptmann Ming-Hsuan Yang Yuan Hao Irfan Essa Lu Jiang DiffM VGen 40 228 0 10 Dec 2022
Nonlinear controllability and function representation by neural stochastic differential equations Tanya Veeravalli Maxim Raginsky DiffM 32 2 0 01 Dec 2022
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations Simon Koop M. Peletier J. Portegies Vlado Menkovski DiffM 35 1 0 17 Nov 2022
Imagen Video: High Definition Video Generation with Diffusion Models Jonathan Ho William Chan Chitwan Saharia Jay Whang Ruiqi Gao ... Diederik P. Kingma Ben Poole Mohammad Norouzi David J. Fleet Tim Salimans VGen 67 1,480 0 05 Oct 2022
Unifying Generative Models with GFlowNets and Beyond Dinghuai Zhang Ricky T. Q. Chen Nikolay Malkin Yoshua Bengio BDL AI4CE 59 25 0 06 Sep 2022
Continuous-time Particle Filtering for Latent Stochastic Differential Equations Ruizhi Deng Greg Mori Andreas M. Lehrmann BDL 30 0 0 01 Sep 2022
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations Jongwan Kim Dongjin Lee Byunggook Na Seongsik Park Jeonghee Jo Sung-Hoon Yoon 42 0 0 15 Jun 2022
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models Kamil Deja Anna Kuzina Tomasz Trzciñski Jakub M. Tomczak DiffM 39 25 0 31 May 2022
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings Martin Gonzalez Thibault Defourneau H. Hajri Mihaly Petreczky 31 2 0 24 May 2022
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia William Chan Saurabh Saxena Lala Li Jay Whang ... Raphael Gontijo-Lopes Tim Salimans Jonathan Ho David J Fleet Mohammad Norouzi VLM 113 5,817 0 23 May 2022
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics Takeshi Koshizuka Issei Sato 42 6 0 11 Apr 2022
Video Diffusion Models Jonathan Ho Tim Salimans Alexey A. Gritsenko William Chan Mohammad Norouzi David J. Fleet DiffM VGen 90 1,526 0 07 Apr 2022
An end-to-end deep learning approach for extracting stochastic dynamical systems with $α$ -stable Lévy noise Cheng Fang Yubin Lu Ting Gao Jinqiao Duan 59 16 0 31 Jan 2022
Heavy Ball Neural Ordinary Differential Equations Hedi Xia Vai Suliafu H. Ji T. Nguyen Andrea L. Bertozzi Stanley J. Osher Bao Wang 45 56 0 10 Oct 2021
Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas Yubin Lu Yang Li Jinqiao Duan 21 16 0 28 Aug 2021
GRAND: Graph Neural Diffusion B. Chamberlain J. Rowbottom Maria I. Gorinova Stefan Webb Emanuele Rossi M. Bronstein GNN 54 254 0 21 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 30 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 41 188 0 05 Jun 2021
Efficient and Accurate Gradients for Neural SDEs Patrick Kidger James Foster Xuechen Li Terry Lyons DiffM 31 61 0 27 May 2021
Deep limits and cut-off phenomena for neural networks B. Avelin A. Karlsson AI4CE 43 2 0 21 Apr 2021
Meta-Solver for Neural Ordinary Differential Equations Julia Gusak A. Katrutsa Talgat Daulbaev A. Cichocki Ivan Oseledets 16 2 0 15 Mar 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 48 485 0 08 Mar 2021