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检索条件"机构=and Program in Applied and Computational Mathematics"
1038 条 记 录,以下是501-510 订阅
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Precise algorithms to compute surface correlation functions of two-phase heterogeneous media and their applications
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Physical Review E 2018年 第1期98卷 013307-013307页
作者: Zheng Ma Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA
The quantitative characterization of the microstructure of random heterogeneous media in d-dimensional Euclidean space Rd via a variety of n-point correlation functions is of great importance, since the respective inf... 详细信息
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Defining and benchmarking open problems in single-cell analysis
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Nature biotechnology 2025年 1-6页
作者: Malte D Luecken Scott Gigante Daniel B Burkhardt Robrecht Cannoodt Daniel C Strobl Nikolay S Markov Luke Zappia Giovanni Palla Wesley Lewis Daniel Dimitrov Michael E Vinyard D S Magruder Michaela F Mueller Alma Andersson Emma Dann Qian Qin Dominik J Otto Michal Klein Olga Borisovna Botvinnik Louise Deconinck Kai Waldrant Sai Nirmayi Yasa Artur Szałata Andrew Benz Zhijian Li Jonathan M Bloom Angela Oliveira Pisco Julio Saez-Rodriguez Drausin Wulsin Luca Pinello Yvan Saeys Fabian J Theis Smita Krishnaswamy Institute of Computational Biology Helmholtz Munich Neuherberg Germany. Institute of Lung Health & Immunity Helmholtz Munich Member of the German Center for Lung Research (DZL) Munich Germany. Immunai New York USA. NVIDIA Santa Clara CA USA. Data Intuitive Lebbeke Belgium. Data Mining and Modelling for Biomedicine group VIB Center for Inflammation Research Ghent Belgium. Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium. Institute of Clinical Chemistry and Pathobiochemistry School of Medicine Technical University of Munich Munich Germany. TUM School of Life Sciences Weihenstephan Technical University of Munich Munich Germany. Division of Pulmonary and Critical Care Medicine Feinberg School of Medicine Northwestern University Chicago IL USA. Department of Mathematics School of Computing Information and Technology Technical University of Munich Munich Germany. Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Faculty of Medicine and Heidelberg University Hospital Institute for Computational Biomedicine Heidelberg University Heidelberg Germany. Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Molecular Pathology Unit Center for Cancer Research Massachusetts General Hospital Boston MA USA. Department of Computer Science Yale University New Haven CT USA. Genentech Inc South San Francisco CA USA. Gene Technology Royal Institute of Technology (KTH) Stockholm Sweden. Science for Life Laboratory (SciLifeLab) Solna Sweden. Wellcome Sanger Institute Cambridge UK. Basic Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Translational Data Science IRC Fred Hutchinson Cancer Center Seattle WA USA. Apple Paris France. Data Sciences Platform
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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease
Research Square
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Research Square 2021年
作者: Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States
The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from... 详细信息
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Active learning of uniformly accurate inter-atomic potentials for materials simulation
arXiv
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arXiv 2018年
作者: Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China
An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular... 详细信息
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Deterministic guarantees for Burer–Monteiro factorizations of smooth semidefinite programs
arXiv
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arXiv 2018年
作者: Boumal, Nicolas Voroninski, Vladislav Bandeira, Afonso S. Mathematics Department Program in Applied and Computational Mathematics Princeton University Department of Mathematics Center for Data Science Courant Institute of Mathematical Sciences New York University
We consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix X of size n. Following the Burer–Monteiro approach, we optimize a factor Y of size n&... 详细信息
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Adaptive coupling of a deep neural network potential to a classical force field
arXiv
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arXiv 2018年
作者: Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science and Beijing International Center for Mathematical Research Peking University China Beijing Institute of Big Data Research Beijing100871 China
An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM ... 详细信息
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DeePCG: Constructing coarse-grained models via deep neural networks
arXiv
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arXiv 2018年
作者: Weinan, E. Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Caep Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton Institute for Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-G... 详细信息
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How SGD selects the global minima in over-parameterized learning: a dynamical stability perspective  18
How SGD selects the global minima in over-parameterized lear...
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Proceedings of the 32nd International Conference on Neural Information Processing Systems
作者: Lei Wu Chao Ma Weinan E. School of Mathematical Sciences Peking University Beijing P.R. China Program in Applied and Computational Mathematics Princeton University Princeton NJ Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton NJ and Beijing Institute of Big Data Research Beijing P.R. China
The question of which global minima are accessible by a stochastic gradient decent (SGD) algorithm with specific learning rate and batch size is studied from the perspective of dynamical stability. The concept of non-...
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The Nystrom Extension for Signals Defined on a Graph
The Nystrom Extension for Signals Defined on a Graph
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Ayelet Heimowitz Yonina C. Eldar The Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Technion Haifa Israel
In this paper we introduce a computationally efficient solution to the problem of graph signal interpolation. Our solution is derived using the Nyström extension and is due to the properties of the Markov matrix ... 详细信息
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MONGE-AMPÈRE FLOW FOR GENERATIVE MODELING
arXiv
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arXiv 2018年
作者: Zhang, Linfeng Weinan, E. Wang, Lei Program in Applied and Computational Mathematics Princeton University United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University United States Center for Data Science Peking University Beijing Institute of Big Data Research Beijing100871 China Institute of Physics Chinese Academy of Sciences Beijing100190 China
We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the late... 详细信息
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