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While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain. This is especially true when trying to understand the impact of model design choices,…

Machine Learning · Computer Science 2023-12-08 Henry Kvinge , Grayson Jorgenson , Davis Brown , Charles Godfrey , Tegan Emerson

This chapter offers a detailed survey on intrinsically localized frames and the corresponding matrix representation of operators. We re-investigate the properties of localized frames and the associated Banach spaces in full detail. We…

Functional Analysis · Mathematics 2019-03-27 Peter Balazs , Karlheinz Gröchenig

We consider the decomposition of bounded linear operators on Hilbert spaces in terms of functions forming frames. Similar to the singular-value decomposition, the resulting frame decompositions encode information on the structure and…

Numerical Analysis · Mathematics 2021-05-26 Simon Hubmer , Ronny Ramlau

We use time-frequency methods for the study of Fourier Integral operators (FIOs). In this paper we shall show that Gabor frames provide very efficient representations for a large class of FIOs. Indeed, similarly to the case of shearlets and…

Analysis of PDEs · Mathematics 2016-06-28 Elena Cordero , Fabio Nicola , Luigi Rodino

Let $\varphi(x_1,\ldots,x_h,y) = u_1x_1 + \cdots + u_hx_h+vy$ be a linear form with nonzero integer coefficients $u_1,\ldots, u_h, v.$ Let $\mathcal{A} = (A_1,\ldots, A_h)$ be an $h$-tuple of finite sets of integers and let $B$ be an…

Number Theory · Mathematics 2021-12-30 Melvyn B. Nathanson

The aim of this work is to study frame theory in quaternionic Hilbert spaces. We provide a characterization of frames in these spaces through the associated operators. Additionally, we examine frames of the form $\{Lu_i\}_{i \in I}$, where…

Functional Analysis · Mathematics 2024-11-07 Najib Khachiaa

We investigate systems of the form $\{A^tg:g\in\mathcal{G},t\in[0,L]\}$ where $A \in B(\mathcal{H})$ is a normal operator in a separable Hilbert space $\mathcal{H}$, $\mathcal{G}\subset \mathcal{H}$ is a countable set, and $L$ is a positive…

Functional Analysis · Mathematics 2019-02-22 Akram Aldroubi , Longxiu Huang , Armenak Petrosyan

Dynamical Sampling of frames and tensor products are important topics in harmonic analysis. This paper combines the concepts of dynamical sampling of frames and the Carleson condition in the tensor product of Hardy spaces. Initially we…

Functional Analysis · Mathematics 2023-08-23 Nabin Kumar Sahu , Vishesh Rajput

Recent work in Dynamical Sampling has been centered on characterizing frames obtained by the orbit of a vector under a bounded operator. We prove a necessary and sufficient condition for a pair of bounded commuting operators on a separable…

Functional Analysis · Mathematics 2025-07-10 Victor Bailey , Carlos Cabrelli

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by flow-based motion…

Computer Vision and Pattern Recognition · Computer Science 2022-07-20 Chengxu Liu , Huan Yang , Jianlong Fu , Xueming Qian

In 2016 Aldroubi et al. constructed the first class of frames having the form $\{T^k\varphi \}_{k=0}^\infty$ for a bounded linear operator on the underlying Hilbert space. In this paper we show that a subclass of these frames has a number…

Functional Analysis · Mathematics 2023-12-20 Ole Christensen , Marzieh Hasannasab , Friedrich Philipp , Diana Stoeva

Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision, including view-point in activity analysis, articulation in shape analysis, and measurement invariance in…

Computer Vision and Pattern Recognition · Computer Science 2018-07-30 Anirudh Som , Kowshik Thopalli , Karthikeyan Natesan Ramamurthy , Vinay Venkataraman , Ankita Shukla , Pavan Turaga

For finding the numerical solution of operator equations in many applications a decomposition in subspaces is needed. Therefore, it is necessary to extend the known method of matrix representation to the utilization of fusion frames. In…

Functional Analysis · Mathematics 2020-07-14 Peter Balazs , Mitra Shamsabadi , Ali Akbar Arefijamaal , Chilles Gardon

Approximately dual frames as a generalization of duality notion in Hilbert spaces have applications in Gabor systems, wavelets, coorbit theory and sensor modeling. In recent years, the computing of the associated deviations of the canonical…

Functional Analysis · Mathematics 2019-05-02 M. Hajiabootorabi , H. Javanshiri , M. R. Mardanbeigi

Thus far, sparse representations have been exploited largely in the context of robustly estimating functions in a noisy environment from a few measurements. In this context, the existence of a basis in which the signal class under…

Data Structures and Algorithms · Computer Science 2009-06-26 Mohamed-Ali Belabbas , Patrick J. Wolfe

This investigation seeks to establish the practicality of numerical frame approximations. Specifically, it develops a new method to approximate the inverse frame operator and analyzes its convergence properties. It is established that…

Numerical Analysis · Mathematics 2012-03-30 Guohui Song , Anne Gelb

The form-factor bootstrap approach is applied to the perturbed minimal models $M_{2,2n+3}$ in the direction of the primary field $\phi_{1,3}$. These theories are integrable and contain $n$ massive scalar particles, whose $S$--matrix is…

High Energy Physics - Theory · Physics 2009-10-28 A. Koubek

Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives. In this work, we leverage this property to perform Video Frame Interpolation (VFI) by explicitly…

Computer Vision and Pattern Recognition · Computer Science 2022-06-23 Weihao Zhuang , Tristan Hascoet , Ryoichi Takashima , Tetsuya Takiguchi

A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces…

Machine Learning · Computer Science 2021-02-11 Diane Bouchacourt , Mark Ibrahim , Stéphane Deny

Dynamic sampling mechanisms in deep learning architectures have demonstrated utility across many computer vision models, though the theoretical analysis of these structures has not yet been unified. In this paper we connect the various…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Dario Morle , Reid Zaffino