標(biāo)題: Titlebook: Explorations in the Mathematics of Data Science; The Inaugural Volume Simon Foucart,Stephan Wojtowytsch Book 2024 The Editor(s) (if applica [打印本頁] 作者: 爆裂 時(shí)間: 2025-3-21 19:07
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作者: 原諒 時(shí)間: 2025-3-21 22:56 作者: limber 時(shí)間: 2025-3-22 03:36 作者: 賞錢 時(shí)間: 2025-3-22 08:10
S-procedure Relaxation: A Case of Exactness Involving Chebyshev Centers,particular instance where the quadratic constraints involve orthogonal projectors. Our argument exploits a previous work of ours, where exact Chebyshev centers were obtained in a different way. We conclude by stating some open questions and by commenting on other recent results in optimal recovery.作者: MOAT 時(shí)間: 2025-3-22 10:39 作者: Esophagitis 時(shí)間: 2025-3-22 14:37
CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D,llelism and deploy our code on modern high-performance computing architectures. Our solver allows us to solve clinically relevant problems in under four seconds on a single GPU. It can also be applied to large-scale 3D imaging applications with data that is discretized on meshes with billions of vox作者: Esophagitis 時(shí)間: 2025-3-22 19:36
S-procedure Relaxation: A Case of Exactness Involving Chebyshev Centers,ions on the functions to be learned. Working in a finite-dimensional Hilbert space, we consider model assumptions based on approximability and observation inaccuracies modeled as additive errors bounded in .. We focus on the local recovery problem, which amounts to the determination of Chebyshev cen作者: 萬神殿 時(shí)間: 2025-3-22 23:34 作者: FIG 時(shí)間: 2025-3-23 04:13 作者: LAITY 時(shí)間: 2025-3-23 05:52 作者: inhibit 時(shí)間: 2025-3-23 11:04 作者: Efflorescent 時(shí)間: 2025-3-23 14:24
Learning Collective Behaviors from Observation, designed to elucidate emergent phenomena within intricate systems of interacting agents. Our approach not only ensures theoretical convergence guarantees but also exhibits computational efficiency when handling high-dimensional observational data. The methods adeptly reconstruct both first- and sec作者: monogamy 時(shí)間: 2025-3-23 21:43
Provably Accelerating Ill-Conditioned Low-Rank Estimation via Scaled Gradient Descent, Even with Ov corrupted, linear measurements. Through the lens of matrix and tensor factorization, one of the most popular approaches is to employ simple iterative algorithms such as gradient descent (GD) to recover the low-rank factors directly, which allow for small memory and computation footprints. However, 作者: 獎(jiǎng)牌 時(shí)間: 2025-3-24 01:46
CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D,age registration is a nonlinear inverse problem. It is about computing a spatial mapping from one image of the same object or scene to another. In diffeomorphic image registration, the set of admissible spatial transformations is restricted to maps that are smooth, are one-to-one, and have a smooth 作者: Nebulizer 時(shí)間: 2025-3-24 04:17 作者: 相一致 時(shí)間: 2025-3-24 08:41 作者: Gratuitous 時(shí)間: 2025-3-24 12:18
Book 2024ty. Chapters are based on talks from CAMDA’s inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those ro作者: 沙草紙 時(shí)間: 2025-3-24 16:47 作者: occurrence 時(shí)間: 2025-3-24 22:52 作者: finite 時(shí)間: 2025-3-25 02:02
Linearly Embedding Sparse Vectors from , to , via Deterministic Dimension-Reducing Maps, strategy, is quasideterministic and applies in the real setting. The second one, exploiting Golomb rulers, is explicit and applies to the complex setting. As a stepping stone, an explicit isometric embedding from . to . is presented. Finally, the extension of the problem from sparse vectors to low-rank matrices is raised as an open question.作者: APEX 時(shí)間: 2025-3-25 05:17 作者: 擺動(dòng) 時(shí)間: 2025-3-25 09:04 作者: 拖網(wǎng) 時(shí)間: 2025-3-25 15:41 作者: 針葉樹 時(shí)間: 2025-3-25 17:10
https://doi.org/10.1007/978-3-658-11649-1onally projected linear maps..We further show that fully connected and residual networks of large depth with polynomial activation functions can approximate any polynomial under certain width requirements. All proofs are entirely elementary.作者: 脫落 時(shí)間: 2025-3-25 23:59 作者: padding 時(shí)間: 2025-3-26 01:24
https://doi.org/10.1007/978-3-642-72043-7lows for reduced storage and asymptotic speed ups for our solver via sparse matrix computations. We conclude the article with the results of computational experiments performed with genomic datasets. These experiments illustrate the significant speed ups obtained by GETS over .’s implementation of the Lawson Hanson algorithm.作者: Hormones 時(shí)間: 2025-3-26 05:59
Richard Colmorn,Michael Hülsmann noise, and (3) the heavy-ball ODE. In the case of stochastic gradient descent, the summability of . is used to prove that . almost surely—an improvement on the convergence almost surely up to a subsequence which follows from the . decay estimate.作者: Obliterate 時(shí)間: 2025-3-26 10:52 作者: CLAIM 時(shí)間: 2025-3-26 15:17
Learning Collective Behaviors from Observation,observations in agent systems. The foundational aspect of our learning methodologies resides in the formulation of tailored loss functions using the variational inverse problem approach, inherently equipping our methods with dimension reduction capabilities.作者: geometrician 時(shí)間: 2025-3-26 20:29 作者: 誘騙 時(shí)間: 2025-3-26 21:39
A Qualitative Difference Between Gradient Flows of Convex Functions in Finite- and Infinite-Dimensi noise, and (3) the heavy-ball ODE. In the case of stochastic gradient descent, the summability of . is used to prove that . almost surely—an improvement on the convergence almost surely up to a subsequence which follows from the . decay estimate.作者: overwrought 時(shí)間: 2025-3-27 05:05 作者: lymphedema 時(shí)間: 2025-3-27 05:53 作者: 征兵 時(shí)間: 2025-3-27 11:06
https://doi.org/10.1007/978-3-322-90326-6ions on the functions to be learned. Working in a finite-dimensional Hilbert space, we consider model assumptions based on approximability and observation inaccuracies modeled as additive errors bounded in .. We focus on the local recovery problem, which amounts to the determination of Chebyshev cen作者: 善變 時(shí)間: 2025-3-27 17:34
Benjamin Krischan Schulte,Andrea Hansenh width ., depth ., and Lipschitz activation functions. We show that modulo logarithmic factors, rates better than entropy numbers’ rates are possibly attainable only for neural networks for which the depth ., and that there is no gain if we fix the depth and let the width ..作者: DUCE 時(shí)間: 2025-3-27 18:50 作者: Heart-Rate 時(shí)間: 2025-3-28 01:51
Samuelson Appau,Samuel K. Bonsuarly to the standard (. to .) restricted isometry property, such constructions can be found in the regime ., at least in theory. With effectiveness of implementation in mind, two simple constructions are presented in the less pleasing but still relevant regime .. The first one, executing a Las Vegas作者: larder 時(shí)間: 2025-3-28 03:04 作者: 冒煙 時(shí)間: 2025-3-28 08:06
Zusammenfassung des dritten Teils designed to elucidate emergent phenomena within intricate systems of interacting agents. Our approach not only ensures theoretical convergence guarantees but also exhibits computational efficiency when handling high-dimensional observational data. The methods adeptly reconstruct both first- and sec作者: interrogate 時(shí)間: 2025-3-28 13:14
https://doi.org/10.1007/978-3-030-87556-5 corrupted, linear measurements. Through the lens of matrix and tensor factorization, one of the most popular approaches is to employ simple iterative algorithms such as gradient descent (GD) to recover the low-rank factors directly, which allow for small memory and computation footprints. However, 作者: 連接 時(shí)間: 2025-3-28 15:34 作者: Sciatica 時(shí)間: 2025-3-28 19:16
https://doi.org/10.1007/978-3-642-72043-7overy is performed using the Lawson Hanson algorithm. To enhance the computational speed of this algorithm, we offer GETS: a GEnomic Tree based Sparse solver. We exploit the inherent structure of the genomic problem to uncover an evolutionary family tree type relationship between the species. This g作者: Deject 時(shí)間: 2025-3-29 00:22
Richard Colmorn,Michael Hülsmannlow case, we prove the following: .This improves on the commonly reported . rate (at least for the lower limit) and provides a sharp characterization of the energy decay law. We also note that it is impossible to establish a rate . for the full limit for any function . which satisfies ., even asympt作者: 路標(biāo) 時(shí)間: 2025-3-29 06:05
https://doi.org/10.1007/978-3-031-66497-7Approximation Theory; Learning Theory; Compressive Sensing; Neural Networks; Center for Approximation an
