diffusion process

q-Learning in Continuous Time JMLR 2023 Stable Target Field for Reduced Variance Score Estimation in Diffusion Models ICLR 2023 Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network ICML 2023 Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms COLT 2023 A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs NeurIPS 2022 Exact simulation of diffusion first exit times: algorithm acceleration JMLR 2022 Hessian-Free High-Resolution Nesterov Acceleration For Sampling ICML 2022 Robust Combination of Distributed Gradients Under Adversarial Perturbations CVPR 2022 Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations ICML 2022 Stationary Diffusion State Neural Estimation for Multiview Clustering AAAI 2022 Contrastive learning of strong-mixing continuous-time stochastic processes AISTATS 2021 De-randomizing MCMC dynamics with the diffusion Stein operator NeurIPS 2021 Doubly infinite residual neural networks: a diffusion process approach JMLR 2021 GRAND: Graph Neural Diffusion ICML 2021 Infinitely deep neural networks as diffusion processes AISTATS 2020 Joint Manifold Diffusion for Combining Predictions on Decoupled Observations CVPR 2019 Learning by Unsupervised Nonlinear Diffusion JMLR 2019 Privacy Amplification by Mixing and Diffusion Mechanisms NeurIPS 2019 Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification CVPR 2019 Variational Annealing of GANs: A Langevin Perspective ICML 2019 Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting ICLR 2018 Capacity Releasing Diffusion for Speed and Locality ICML 2017 Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis COLT 2017 Quantifying the accuracy of approximate diffusions and Markov chains AISTATS 2017 A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes JMLR 2016