Applied and Computational Harmonic Analysis最新文献

Duality for neural networks through Reproducing Kernel Banach Spaces 利用核Banach空间再现神经网络的对偶性

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-27 DOI: 10.1016/j.acha.2025.101765

Len Spek , Tjeerd Jan Heeringa , Felix Schwenninger , Christoph Brune

引用次数: 0

Controlled learning of pointwise nonlinearities in neural-network-like architectures 类神经网络结构中点非线性的受控学习

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-25 DOI: 10.1016/j.acha.2025.101764

Michael Unser, Alexis Goujon, Stanislas Ducotterd

{"title":"Controlled learning of pointwise nonlinearities in neural-network-like architectures","authors":"Michael Unser, Alexis Goujon, Stanislas Ducotterd","doi":"10.1016/j.acha.2025.101764","DOIUrl":"10.1016/j.acha.2025.101764","url":null,"abstract":"<div><div>We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order total variation of each trainable activation. The slope constraints allow us to impose properties such as 1-Lipschitz stability, firm non-expansiveness, and monotonicity/invertibility. These properties are crucial to ensure the proper functioning of certain classes of signal-processing algorithms (e.g., plug-and-play schemes, unrolled proximal gradient, invertible flows). We prove that the global optimum of the stated constrained-optimization problem is achieved with nonlinearities that are adaptive nonuniform linear splines. We then show how to solve the resulting function-optimization problem numerically by representing the nonlinearities in a suitable (nonuniform) B-spline basis. Finally, we illustrate the use of our framework with the data-driven design of (weakly) convex regularizers for the denoising of images and the resolution of inverse problems.</div></div>","PeriodicalId":55504,"journal":{"name":"Applied and Computational Harmonic Analysis","volume":"77 ","pages":"Article 101764"},"PeriodicalIF":2.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement 社会和司法约束下深度学习的数学算法设计：算法透明度要求

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-24 DOI: 10.1016/j.acha.2025.101763

Holger Boche , Adalbert Fono , Gitta Kutyniok

{"title":"Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement","authors":"Holger Boche , Adalbert Fono , Gitta Kutyniok","doi":"10.1016/j.acha.2025.101763","DOIUrl":"10.1016/j.acha.2025.101763","url":null,"abstract":"<div><div>Deep learning still has drawbacks regarding trustworthiness, which describes a comprehensible, fair, safe, and reliable method. To mitigate the potential risk of AI, clear obligations associated with trustworthiness have been proposed via regulatory guidelines, e.g., in the European AI Act. Therefore, a central question is to what extent trustworthy deep learning can be realized. Establishing the described properties constituting trustworthiness requires that the factors influencing an algorithmic computation can be retraced, i.e., the algorithmic implementation is transparent. Motivated by the observation that the current evolution of deep learning models necessitates a change in computing technology, we derive a mathematical framework that enables us to analyze whether a transparent implementation in a computing model is feasible. The core idea is to formalize and subsequently relate the properties of a transparent algorithmic implementation to the mathematical model of the computing platform, thereby establishing verifiable criteria.</div><div>We exemplarily apply our trustworthiness framework to analyze deep learning approaches for inverse problems in digital and analog computing models represented by Turing and Blum-Shub-Smale machines, respectively. Based on previous results, we find that Blum-Shub-Smale machines have the potential to establish trustworthy solvers for inverse problems under fairly general conditions, whereas Turing machines cannot guarantee trustworthiness to the same degree.</div></div>","PeriodicalId":55504,"journal":{"name":"Applied and Computational Harmonic Analysis","volume":"77 ","pages":"Article 101763"},"PeriodicalIF":2.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy of compact operators with applications to Landau-Pollak-Slepian theory and Sobolev spaces 紧算子的熵及其在Landau-Pollak-Slepian理论和Sobolev空间中的应用

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-21 DOI: 10.1016/j.acha.2025.101762

Thomas Allard, Helmut Bölcskei

引用次数: 0

An efficient spatial discretization of spans of multivariate Chebyshev polynomials 多元切比雪夫多项式跨度的有效空间离散化

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-20 DOI: 10.1016/j.acha.2025.101761

Lutz Kämmerer

{"title":"An efficient spatial discretization of spans of multivariate Chebyshev polynomials","authors":"Lutz Kämmerer","doi":"10.1016/j.acha.2025.101761","DOIUrl":"10.1016/j.acha.2025.101761","url":null,"abstract":"<div><div>For an arbitrary given span of high dimensional multivariate Chebyshev polynomials, an approach to construct spatial discretizations is presented, i.e., the construction of a sampling set that allows for the unique reconstruction of each polynomial of this span.</div><div>The approach presented here combines three different types of efficiency. First, the construction of a spatial discretization should be computationally efficient with respect to the dimension of the span of the Chebyshev polynomials. Second, the constructed discretization should be sample efficient, i.e., the number of sampling nodes within the constructed discretization should be reasonably low. Third, there should be an efficient algorithm for the unique reconstruction of a polynomial from given sampling values at the sampling nodes of the discretization.</div><div>The first two mentioned types of efficiency are also present in constructions based on random sampling nodes, but the lack of structure here causes the inefficiency of the reconstruction method. Our approach uses a combination of cosine transformed rank-1 lattices whose structure allows for applications of univariate fast Fourier transforms for the reconstruction algorithm and is thus a priori efficiently realizable.</div><div>Besides the theoretical estimates of numbers of sampling nodes and failure probabilities due to a random draw of the used lattices, we present several improvements of the basic design approach that significantly increases its practical applicability. Numerical tests, which discretize spans of multivariate Chebyshev polynomials depending on up to more than 50 spatial variables, corroborate the theoretical results and the significance of the improvements.</div></div>","PeriodicalId":55504,"journal":{"name":"Applied and Computational Harmonic Analysis","volume":"77 ","pages":"Article 101761"},"PeriodicalIF":2.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An inverse problem for Dirac systems on p-star-shaped graphs p星形图上Dirac系统的反问题

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-20 DOI: 10.1016/j.acha.2025.101760

Yu Ping Wang , Yan-Hsiou Cheng

引用次数: 0

Error estimate of the u-series method for molecular dynamics simulations 分子动力学模拟u系列方法的误差估计

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-03-14 DOI: 10.1016/j.acha.2025.101759

Jiuyang Liang , Zhenli Xu , Qi Zhou

引用次数: 0

The Large Deviation Principle for W-random spectral measures

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-02-21 DOI: 10.1016/j.acha.2025.101756

Mahya Ghandehari , Georgi S. Medvedev

引用次数: 0

Efficient identification of wide shallow neural networks with biases

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-02-17 DOI: 10.1016/j.acha.2025.101749

Massimo Fornasier , Timo Klock , Marco Mondelli , Michael Rauchensteiner

{"title":"Efficient identification of wide shallow neural networks with biases","authors":"Massimo Fornasier , Timo Klock , Marco Mondelli , Michael Rauchensteiner","doi":"10.1016/j.acha.2025.101749","DOIUrl":"10.1016/j.acha.2025.101749","url":null,"abstract":"<div><div>The identification of the parameters of a neural network from finite samples of input-output pairs is often referred to as the <em>teacher-student model</em>, and this model has represented a popular framework for understanding training and generalization. Even if the problem is NP-complete in the worst case, a rapidly growing literature – after adding suitable distributional assumptions – has established finite sample identification of two-layer networks with a number of neurons <span><math><mi>m</mi><mo>=</mo><mi>O</mi><mo>(</mo><mi>D</mi><mo>)</mo></math></span>, <em>D</em> being the input dimension. For the range <span><math><mi>D</mi><mo><</mo><mi>m</mi><mo><</mo><msup><mrow><mi>D</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> the problem becomes harder, and truly little is known for networks parametrized by biases as well. This paper fills the gap by providing efficient algorithms and rigorous theoretical guarantees of finite sample identification for such wider shallow networks with biases. Our approach is based on a two-step pipeline: first, we recover the direction of the weights, by exploiting second order information; next, we identify the signs by suitable algebraic evaluations, and we recover the biases by empirical risk minimization via gradient descent. Numerical results demonstrate the effectiveness of our approach.</div></div>","PeriodicalId":55504,"journal":{"name":"Applied and Computational Harmonic Analysis","volume":"77 ","pages":"Article 101749"},"PeriodicalIF":2.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kadec-type theorems for sampled group orbits 抽样群轨道的kadec型定理

IF 2.6 2区数学

Applied and Computational Harmonic Analysis Pub Date : 2025-01-10 DOI: 10.1016/j.acha.2025.101748

Ilya Krishtal, Brendan Miller

引用次数: 0