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Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application. This paper presents a novel approach for approximating MBR…

Computation and Language · Computer Science 2024-06-06 Firas Trabelsi , David Vilar , Mara Finkelstein , Markus Freitag

The low-rank matrix recovery problem often arises in various fields, including signal processing, machine learning, and imaging science. The Riemannian gradient descent (RGD) algorithm has proven to be an efficient algorithm for solving…

Optimization and Control · Mathematics 2023-05-05 Fengmiao Bian , Jian-Feng Cai , Rui Zhang

We study the distinguishability of linearized Reed-Solomon (LRS) codes by defining and analyzing analogs of the square-code and the Overbeck distinguisher for classical Reed-Solomon and Gabidulin codes, respectively. Our main results show…

Information Theory · Computer Science 2023-04-04 Felicitas Hörmann , Hannes Bartz , Anna-Lena Horlemann

We consider linear rank-metric codes in $\mathbb F_{q^m}^n$. We show that the properties of being MRD (maximum rank distance) and non-Gabidulin are generic over the algebraic closure of the underlying field, which implies that over a large…

Information Theory · Computer Science 2018-03-13 Alessandro Neri , Anna-Lena Horlemann-Trautmann , Tovohery Randrianarisoa , Joachim Rosenthal

The aim of this note (as well as of the course itself) is to give a largely self-contained proof of two of the main results in the field of low-rank matrix recovery. This field aims for identification of low-rank matrices from only limited…

Functional Analysis · Mathematics 2016-09-27 Jan Vybiral

In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation.…

Computer Vision and Pattern Recognition · Computer Science 2020-02-19 Mathews Jacob , Merry P. Mani , Jong Chul Ye

The task of reconstructing a low rank matrix from incomplete linear measurements arises in areas such as machine learning, quantum state tomography and in the phase retrieval problem. In this note, we study the particular setup that the…

Information Theory · Computer Science 2016-12-12 Holger Rauhut , Ulrich Terstiege

While random linear network coding is a powerful tool for disseminating information in communication networks, it is highly susceptible to errors caused by various sources. Due to error propagation, errors greatly deteriorate the throughput…

Information Theory · Computer Science 2016-11-15 Ning Chen , Zhiyuan Yan , Maximilien Gadouleau , Ying Wang , Bruce W. Suter

In color image processing, image completion aims to restore missing entries from the incomplete observation image. Recently, great progress has been made in achieving completion by approximately solving the rank minimization problem. In…

Image and Video Processing · Electrical Eng. & Systems 2021-07-06 Liqiao Yang , Jifei Miao , Kit Ian Kou

Node failures are inevitable in distributed storage systems (DSS). To enable efficient repair when faced with such failures, two main techniques are known: Regenerating codes, i.e., codes that minimize the total repair bandwidth; and codes…

This paper investigates the decoding of certain Gabidulin codes that were transmitted over a channel with space-symmetric errors. Space-symmetric errors are additive error matrices that have the property that their column and row spaces are…

Information Theory · Computer Science 2021-02-05 Thomas Jerkovits , Vladimir Sidorenko , Antonia Wachter-Zeh

Local Reconstruction Codes (LRCs) allow for recovery from a small number of erasures in a local manner based on just a few other codeword symbols. A maximally recoverable (MR) LRC offers the best possible blend of such local and global…

Information Theory · Computer Science 2018-08-15 Venkatesan Guruswami , Lingfei Jin , Chaoping Xing

We construct an explicit family of linear rank-metric codes over any field ${\mathbb F}_h$ that enables efficient list decoding up to a fraction $\rho$ of errors in the rank metric with a rate of $1-\rho-\epsilon$, for any desired $\rho \in…

Information Theory · Computer Science 2013-12-03 Venkatesan Guruswami , Carol Wang

Gabidulin codes, serving as the rank-metric counterpart of Reed-Solomon codes, constitute an important class of maximum rank distance (MRD) codes. However, unlike the fruitful positive results about the list decoding of Reed-Solomon codes,…

Information Theory · Computer Science 2024-04-23 Zeyu Guo , Chaoping Xing , Chen Yuan , Zihan Zhang

In this work we present a new criterion to check if a given rank-metric code is a maximum rank distance (MRD) code. Moreover, we derive a criterion to check if a given MRD code is a generalized Gabidulin code. We then use these results to…

Information Theory · Computer Science 2017-10-04 Anna-Lena Horlemann-Trautmann , Kyle Marshall

Over fields of characteristic unequal to $2$, we can identify symmetric matrices with homogeneous polynomials of degree $2$. This allows us to view symmetric rank-metric codes as living inside the space of such polynomials. In this paper,…

Information Theory · Computer Science 2023-03-14 Arthur Bik , Alessandro Neri

This paper presents an algorithm for decoding homogeneous interleaved codes of high interleaving order in the rank metric. The new decoder is an adaption of the Hamming-metric decoder by Metzner and Kapturowski (1990) and guarantees to…

Information Theory · Computer Science 2019-04-19 Sven Puchinger , Julian Renner , Antonia Wachter-Zeh

This work deals with partial MDS (PMDS) codes, a special class of locally repairable codes, used for distributed storage system. We first show that a known construction of these codes, using Gabidulin codes, can be extended to use any…

Information Theory · Computer Science 2018-01-19 Alessandro Neri , Anna-Lena Horlemann-Trautmann

Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geometry of the space, resulting in a highly under-determined…

Computer Vision and Pattern Recognition · Computer Science 2016-05-27 Nauman Shahid , Nathanael Perraudin , Pierre Vandergheynst

We consider the problem of estimation of a low-rank matrix from a limited number of noisy rank-one projections. In particular, we propose two fast, non-convex \emph{proper} algorithms for matrix recovery and support them with rigorous…

Machine Learning · Statistics 2017-05-23 Mohammadreza Soltani , Chinmay Hegde