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Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

12 August 2020

Papers citing "Non-convex Learning via Replica Exchange Stochastic Gradient MCMC"

35 / 35 papers shown

Title
One Period to Rule Them All: Identifying Critical Learning Periods in Deep Networks Vinicius Yuiti Fukase Heitor Gama Bárbara Dias Bueno Lucas Libanio A. H. R. Costa Artur Jordao 12 0 0 19 Jun 2025
Continuous Policy and Value Iteration for Stochastic Control Problems and Its Convergence Qi Feng Gu Wang 10 0 0 09 Jun 2025
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 215 2 0 16 May 2025
Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis Nian Yao Pervez Ali Xihua Tao Lingjiong Zhu 75 1 0 24 Mar 2025
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery Cindy Xiangrui Kong Haoyang Zheng Guang Lin AI4CE 78 0 0 04 Mar 2025
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo Hyunsu Kim G. Nam Chulhee Yun Hongseok Yang Juho Lee BDL UQCV 100 0 0 02 Mar 2025
Enhancing Gradient-based Discrete Sampling via Parallel Tempering Luxu Liang Yuhang Jia Feng Zhou 135 0 0 26 Feb 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 114 0 0 25 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng Guang Lin AI4CE 91 0 0 01 Feb 2025
Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing Giorgio Roffo Carlo Biffi Pietro Salvagnini Andrea Cherubini 55 0 0 05 Jul 2024
Feynman-Kac Operator Expectation Estimator Jingyuan Li Wei Liu 81 0 0 02 Jul 2024
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics Haoyang Zheng Hengrong Du Qi Feng Wei Deng Guang Lin 65 5 0 13 May 2024
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling Patrick Pynadath Riddhiman Bhattacharya Arun Hariharan Ruqi Zhang 77 5 0 27 Feb 2024
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks Christian Moya Amirhossein Mollaali Zecheng Zhang Lu Lu Guang Lin UQCV 91 19 0 23 Feb 2024
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo Haoyang Zheng Wei Deng Christian Moya Guang Lin 87 6 0 22 Jan 2024
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions Zhihao Kong Amirhossein Mollaali Christian Moya Na Lu Guang Lin 74 2 0 28 Nov 2023
Statistical guarantees for stochastic Metropolis-Hastings S. Bieringer Gregor Kasieczka Maximilian F. Steffen Mathias Trabs 80 1 0 13 Oct 2023
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles Izzet Sahin Christian Moya Amirhossein Mollaali Guang Lin Guillermo Paniagua AI4CE 73 17 0 01 Jun 2023
Subsampling Error in Stochastic Gradient Langevin Diffusions Kexin Jin Chenguang Liu J. Latz 68 0 0 23 May 2023
Fast Replica Exchange Stochastic Gradient Langevin Dynamics Guanxun Li Guang Lin Zecheng Zhang Quan Zhou 437 4 0 05 Jan 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 95 21 0 15 Dec 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation Wei Deng Qian Zhang Qi Feng F. Liang Guang Lin 69 4 0 20 Nov 2022
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants Liyao (Mars) Gao J. Nathan Kutz AI4CE 86 22 0 19 Nov 2022
Interacting Contour Stochastic Gradient Langevin Dynamics Wei Deng Siqi Liang Botao Hao Guang Lin F. Liang BDL 79 10 0 20 Feb 2022
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi-An Ma Zhao Song Guang Lin FedML 82 17 0 09 Dec 2021
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs Guang Lin Christian Moya Zecheng Zhang 76 30 0 03 Nov 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim Sotirios Sabanis 97 12 0 28 May 2021
UPANets: Learning from the Universal Pixel Attention Networks Ching-Hsun Tseng Shin-Jye Lee Jianxing Feng Shengzhong Mao Yuping Wu Jia-Yu Shang Mou-Chung Tseng Xiao-Jun Zeng 52 15 0 15 Mar 2021
A New Framework for Variance-Reduced Hamiltonian Monte Carlo Zhengmian Hu Feihu Huang Heng-Chiao Huang 29 0 0 09 Feb 2021
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 76 28 0 19 Oct 2020
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling Difan Zou Pan Xu Quanquan Gu 101 36 0 19 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 74 9 0 02 Oct 2020
flexgrid2vec: Learning Efficient Visual Representations Vectors Ali Hamdi D. Kim Flora D. Salim SSL GNN 88 7 0 30 Jul 2020
Hessian-Free High-Resolution Nesterov Acceleration for Sampling Ruilin Li H. Zha Molei Tao 72 8 0 16 Jun 2020
Stein Self-Repulsive Dynamics: Benefits From Past Samples Mao Ye Zhaolin Ren Qiang Liu 61 8 0 21 Feb 2020