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检索条件"机构=Program in Computer Science and Division of Applied Mathematics"
1692 条 记 录,以下是71-80 订阅
排序:
CONVERGENCE ANALYSIS FOR A NONLOCAL GRADIENT DESCENT METHOD VIA DIRECTIONAL GAUSSIAN SMOOTHING
arXiv
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arXiv 2023年
作者: Tran, Hoang Du, Qiang Zhang, Guannan Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Applied Physics and Applied Mathematics Data Science Institute Columbia University New YorkNY10027 United States
We analyze the convergence of a nonlocal gradient descent method for minimizing a class of high-dimensional non-convex functions, where a directional Gaussian smoothing (DGS) is proposed to define the nonlocal gradien... 详细信息
来源: 评论
Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data  7
Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...
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7th Cyber Security in Networking Conference, CSNet 2023
作者: Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates
This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and ... 详细信息
来源: 评论
Recent advances in particle swarm optimization analysis and understanding 2021  21
Recent advances in particle swarm optimization analysis and ...
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2021 Genetic and Evolutionary Computation Conference, GECCO 2021
作者: Engelbrecht, A P Cleghorn, C W Stellenbosch University Department of Industrial Engineering South Africa Division of Computer Science Stellenbosch University South Africa University of the Witwatersrand School of Computer Science and Applied Mathematics South Africa
来源: 评论
Unbiased Estimation using Underdamped Langevin Dynamics
arXiv
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arXiv 2022年
作者: Ruzayqat, Hamza Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia
In this work we consider the unbiased estimation of expectations w.r.t. probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant. We focus upon developi... 详细信息
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Bayesian Spectral Graph Denoising with Smoothness Prior
Bayesian Spectral Graph Denoising with Smoothness Prior
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Annual Conference on Information sciences and Systems (CISS)
作者: Sam Leone Xingzhi Sun Michael Perlmutter Smita Krishnaswamy Program for Applied Mathematics Yale University Department of Computer Science Yale University Department of Mathematics Boise State University Department of Genetics Yale School of Medicine Wu Tsai Institute Yale University FAIR Meta AI Computational Biology and Bioinformatics Program Yale University
Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is ver...
来源: 评论
Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
arXiv
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arXiv 2024年
作者: Van Lancker, Kelly Díaz, Iván Vansteelandt, Stijn Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Division of Biostatistics Department of Population Health New York University School of Medicine NY United States
In May 2023, the U.S. Food and Drug Administration (FDA) released guidance for industry on "Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products". Covariate adjustment is... 详细信息
来源: 评论
End-to-End Protocol for High-Quality QAOA Parameters with Few Shots
arXiv
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arXiv 2024年
作者: Hao, Tianyi He, Zichang Shaydulin, Ruslan Larson, Jeffrey Pistoia, Marco Global Technology Applied Research JPMorganChase New YorkNY10017 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. For a g... 详细信息
来源: 评论
Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience
arXiv
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arXiv 2023年
作者: Maama, Mohamed Jasra, Ajay Kamatani, Kengo Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan
In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applic... 详细信息
来源: 评论
Numerical evidence against advantage with quantum fidelity kernels on classical data
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Physical Review A 2023年 第6期107卷 062417-062417页
作者: Lucas Slattery Ruslan Shaydulin Shouvanik Chakrabarti Marco Pistoia Sami Khairy Stefan M. Wild Department of Physics University of Illinois Urbana-Champaign Champaign Illinois 61820 USA Mathematics and Computer Science Division Argonne National Laboratory Lemont Illinois 60439 USA Global Technology Applied Research JPMorgan Chase New York New York 10017 USA Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA
Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able t... 详细信息
来源: 评论
Imaging atomic-scale chemistry from fused multi-modal electron microscopy
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npj Computational Materials 2022年 第1期8卷 164-171页
作者: Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States
Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)r... 详细信息
来源: 评论