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We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires…

Machine Learning · Computer Science 2021-03-30 Ameesh Shah , Eric Zhan , Jennifer J. Sun , Abhinav Verma , Yisong Yue , Swarat Chaudhuri

We study the minimum number of minimal codewords in linear codes from the point of view of projective geometry. We derive bounds and in some cases determine the exact values. We also present an extension to minimal subcode supports.

Combinatorics · Mathematics 2023-01-19 Romar dela Cruz , Michael Kiermaier , Sascha Kurz , Alfred Wassermann

Low rank approximation is an important tool used in many applications of signal processing and machine learning. Recently, randomized sketching algorithms were proposed to effectively construct low rank approximations and obtain approximate…

Information Theory · Computer Science 2018-09-11 Shashanka Ubaru , Arya Mazumdar , Yousef Saad

We apply polynomial techniques (linear programming) to obtain lower and upper bounds on the covering radius of spherical designs as function of their dimension, strength, and cardinality. In terms of inner products we improve the lower…

Combinatorics · Mathematics 2020-07-14 Peter Boyvalenkov , Maya Stoyanova

In this paper, based on the theory of defining sets, a class of four-weight or five-weight linear codes over Fp is constructed. The complete weight enumerators of the linear codes are determined by means of Weil sums. In some case, there is…

Information Theory · Computer Science 2020-12-10 Xina Zhang

The question of what can be computed, and how efficiently, are at the core of computer science. Not surprisingly, in distributed systems and networking research, an equally fundamental question is what can be computed in a…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-04-01 Fabian Kuhn , Thomas Moscibroda , Roger Wattenhofer

This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system…

Signal Processing · Electrical Eng. & Systems 2025-10-02 Louis-Adrien Dufrène , Quentin Lampin , Guillaume Larue

The problem of error-control in random linear network coding is considered. A ``noncoherent'' or ``channel oblivious'' model is assumed where neither transmitter nor receiver is assumed to have knowledge of the channel transfer…

Information Theory · Computer Science 2008-03-25 Ralf Koetter , Frank Kschischang

We construct linear codes over the finite field Fq from arbitrary simplicial complexes, establishing a connection between topological properties and fundamental coding parameters. First, we study the behaviour of the weights of codewords…

Information Theory · Computer Science 2026-03-05 Antonio Jesús Lorite López , Daniel Camazón Portela , Juan Antonio López Ramos

Low-density parity check (LDPC) codes are a significant class of classical codes with many applications. Several good LDPC codes have been constructed using random, algebraic, and finite geometries approaches, with containing cycles of…

Quantum Physics · Physics 2016-11-18 Salah A. Aly

In this paper we introduce the class of Spread Codes for the use in random network coding. Spread Codes are based on the construction of spreads in finite projective geometry. The major contribution of the paper is an efficient decoding…

Information Theory · Computer Science 2016-11-17 Felice Manganiello , Elisa Gorla , Joachim Rosenthal

It is reasonable to expect the theory of quantum codes to be simplified in the case of codes of minimum distance 2; thus, it makes sense to examine such codes in the hopes that techniques that prove effective there will generalize. With…

Quantum Physics · Physics 2007-05-23 Eric M. Rains

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

In this paper, we propose a class of linear codes and obtain their weight distribution. Some of these codes are almost optimal. Moreover, several classes of constant composition codes(CCCs) are constructed as subcodes of linear codes.

Information Theory · Computer Science 2017-06-28 Long Yu , Xiusheng Liu

The Galois hull of a linear code is the intersection of itself and its Galois dual code, which has aroused the interest of researchers in these years. In this paper, we study Galois hulls of linear codes. Firstly, the symmetry of the…

Information Theory · Computer Science 2023-01-12 Yang Li , Shixin Zhu

It is well known that, given \(b\ge 0\), finding an $(a,b)$-trapping set with the minimum \(a\) in a binary linear code is NP-hard. In this paper, we demonstrate that this problem can be solved with linear complexity with respect to the…

Information Theory · Computer Science 2026-02-02 Qingqing Peng , Ke Liu , Guiying Yan , Guanghui Wang

Linear network coding requires arithmetic operations over Galois fields, more specifically over finite extension fields. While coding over GF(2) reduces to simple XOR operations, this field is less preferred for practical applications of…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-09-09 Stephan M. Günther , Nicolas Appel , Georg Carle

Linear codes are considered over the ring $\mathbb{Z}_4+v\mathbb{Z}_4$, where $v^2=v$. Gray weight, Gray maps for linear codes are defined and MacWilliams identity for the Gray weight enumerator is given. Self-dual codes, construction of…

Information Theory · Computer Science 2015-01-05 Jian Gao , Yun Gao , Fang-Wei Fu

The generalized Hamming weights (GHWs) are fundamental parameters of linear codes. GHWs are of great interest in many applications since they convey detailed information of linear codes. In this paper, we continue the work of [10] to study…

Information Theory · Computer Science 2017-02-07 Shuxing Li

There has been a recent push in making machine learning models more interpretable so that their performance can be trusted. Although successful, these methods have mostly focused on the deep learning methods while the fundamental…

Machine Learning · Computer Science 2022-06-16 David Steinmann , Matej Zečević , Devendra Singh Dhami , Kristian Kersting