Journal of Machine Learning Research最新文献_第2页

Batch Normalization Preconditioning for Stochastic Gradient Langevin Dynamics 随机梯度朗格万动力学的批归一化预处理

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.220726a

Susanne Lange, Wei Deng, Q. Ye, Guang Lin

引用次数: 2

A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization with NonIsolated Local Minima 具有非孤立局部极小值的非凸优化随机梯度下降法的局部收敛理论

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230106

Taehee Ko and Xiantao Li

引用次数: 0

Efficient Anti-Symmetrization of a Neural Network Layer by Taming the Sign Problem 基于驯服符号问题的神经网络层的有效抗对称

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230703

Nilin Abrahamsen and Lin Lin

引用次数: 0

A Brief Survey on the Approximation Theory for Sequence Modelling 序列建模的近似理论综述

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.221221

Haotian Jiang, Qianxiao Li, Zhong Li null, Shida Wang

引用次数: 0

Reinforcement Learning with Function Approximation: From Linear to Nonlinear 函数逼近的强化学习:从线性到非线性

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230105

Jihao Long and Jiequn Han

引用次数: 0

Why Self-Attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries 为什么自我关注是序列对序列问题的自然表现?从对称角度看问题

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.221206

Chao Ma and Lexing Ying null

引用次数: 0

Selective inference for k-means clustering. k-means 聚类的选择性推理。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-05-01

Yiqun T Chen, Daniela M Witten

引用次数: 0

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. 基于贝叶斯模型的聚类中的维数诅咒。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-04-01

Noirrit Kiran Chandra, Antonio Canale, David B Dunson

{"title":"Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.","authors":"Noirrit Kiran Chandra, Antonio Canale, David B Dunson","doi":"","DOIUrl":"","url":null,"abstract":"Bayesian mixture models are widely used for clustering of high-dimensional data with appropriate uncertainty quantification. However, as the dimension of the observations increases, posterior inference often tends to favor too many or too few clusters. This article explains this behavior by studying the random partition posterior in a non-standard setting with a fixed sample size and increasing data dimensionality. We provide conditions under which the finite sample posterior tends to either assign every observation to a different cluster or all observations to the same cluster as the dimension grows. Interestingly, the conditions do not depend on the choice of clustering prior, as long as all possible partitions of observations into clusters have positive prior probabilities, and hold irrespective of the true data-generating model. We then propose a class of latent mixtures for Bayesian clustering (Lamb) on a set of low-dimensional latent variables inducing a partition on the observed data. The model is amenable to scalable posterior inference and we show that it can avoid the pitfalls of high-dimensionality under mild assumptions. The proposed approach is shown to have good performance in simulation studies and an application to inferring cell types based on scRNAseq.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"24 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RNN-Attention Based Deep Learning for Solving Inverse Boundary Problems in Nonlinear Marshak Waves 基于rnn -注意力的深度学习求解非线性马沙克波反边界问题

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-04-01 DOI: 10.4208/jml.221209

Di Zhao, Weiming Li, Wengu Chen, Peng Song, and Han Wang null

{"title":"RNN-Attention Based Deep Learning for Solving Inverse Boundary Problems in Nonlinear Marshak Waves","authors":"Di Zhao, Weiming Li, Wengu Chen, Peng Song, and Han Wang null","doi":"10.4208/jml.221209","DOIUrl":"https://doi.org/10.4208/jml.221209","url":null,"abstract":". Radiative transfer, described by the radiative transfer equation (RTE), is one of the dominant energy exchange processes in the inertial conﬁnement fusion (ICF) experiments. The Marshak wave problem is an important benchmark for time-dependent RTE. In this work, we present a neural network architecture termed RNN-attention deep learning (RADL) as a surrogate model to solve the inverse boundary problem of the nonlinear Marshak wave in a data-driven fashion. We train the surrogate model by numerical simulation data of the forward problem, and then solve the inverse problem by minimizing the distance between the target solution and the surrogate predicted solution concerning the boundary condition. This minimization is made efﬁcient because the surrogate model by-passes the expensive numerical solution, and the model is differentiable so the gradient-based optimization algorithms are adopted. The effectiveness of our approach is demonstrated by solving the inverse boundary problems of the Marshak wave benchmark in two case studies: where the transport process is modeled by RTE and where it is modeled by its nonlinear diffusion approximation (DA). Last but not least, the importance of using both the RNN and the factor-attention blocks in the RADL model is illustrated, and the data efﬁciency of our model is investigated in this work.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"75 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74640699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds. 紧凑黎曼曼形上具有马特恩协方差的高斯过程推理

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-03-01

Didong Li, Wenpin Tang, Sudipto Banerjee

{"title":"Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds.","authors":"Didong Li, Wenpin Tang, Sudipto Banerjee","doi":"","DOIUrl":"","url":null,"abstract":"Gaussian processes are widely employed as versatile modelling and predictive tools in spatial statistics, functional data analysis, computer modelling and diverse applications of machine learning. They have been widely studied over Euclidean spaces, where they are specified using covariance functions or covariograms for modelling complex dependencies. There is a growing literature on Gaussian processes over Riemannian manifolds in order to develop richer and more flexible inferential frameworks for non-Euclidean data. While numerical approximations through graph representations have been well studied for the Matérn covariogram and heat kernel, the behaviour of asymptotic inference on the parameters of the covariogram has received relatively scant attention. We focus on asymptotic behaviour for Gaussian processes constructed over compact Riemannian manifolds. Building upon a recently introduced Matérn covariogram on a compact Riemannian manifold, we employ formal notions and conditions for the equivalence of two Matérn Gaussian random measures on compact manifolds to derive the parameter that is identifiable, also known as the microergodic parameter, and formally establish the consistency of the maximum likelihood estimate and the asymptotic optimality of the best linear unbiased predictor. The circle is studied as a specific example of compact Riemannian manifolds with numerical experiments to illustrate and corroborate the theory.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"24 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9876354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0