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This essay explores strong data-processing inequalities (SPDI's) as they appear in the work of Evans and Schulman \cite{ES} and von Neumann \cite{vN} on computing with noisy circuits. We first develop the framework in \cite{ES}, which leads…

Information Theory · Computer Science 2025-07-22 Chenyang Sun

This paper presents an analysis of the concept of capacity for noisy com- putations, i.e. functions implemented by unreliable or random devices. An information theoretic model of noisy computation of a perfect function f (measurable…

Information Theory · Computer Science 2016-03-23 Francois Simon

$ \newcommand{\cclass}[1]{{\normalfont\textsf{##1}}} $We show average-case lower bounds for explicit Boolean functions against bounded-depth threshold circuits with a superlinear number of wires. We show that for each integer $d > 1$, there…

Computational Complexity · Computer Science 2018-06-19 Ruiwen Chen , Rahul Santhanam , Srikanth Srinivasan

Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learning high-dimensional parity functions poses significant challenges: in a general setting,…

Machine Learning · Computer Science 2026-05-28 Guillaume Larue , Louis-Adrien Dufrène , Quentin Lampin , Hadi Ghauch , Ghaya Rekaya

A lossy data compression scheme for uniformly biased Boolean messages is investigated via statistical mechanics techniques. We utilize tree-like committee machine (committee tree) and tree-like parity machine (parity tree) whose transfer…

Statistical Mechanics · Physics 2008-08-27 Florent Cousseau , Kazushi Mimura , Toshiaki Omori , Masato Okada

Motivated by data-rich experiments in transcriptional regulation and sensory neuroscience, we consider the following general problem in statistical inference. When exposed to a high-dimensional signal S, a system of interest computes a…

Quantitative Methods · Quantitative Biology 2013-12-16 Justin B. Kinney , Gurinder S. Atwal

Let $\mathsf{TH}_k$ denote the $k$-out-of-$n$ threshold function: given $n$ input Boolean variables, the output is $1$ if and only if at least $k$ of the inputs are $1$. We consider the problem of computing the $\mathsf{TH}_k$ function…

Data Structures and Algorithms · Computer Science 2024-12-24 Ziao Wang , Nadim Ghaddar , Banghua Zhu , Lele Wang

Deep neural networks have been successfully applied to a broad range of problems where overparametrization yields weight matrices which are partially random. A comparison of weight matrix singular vectors to the Porter-Thomas distribution…

Disordered Systems and Neural Networks · Physics 2024-01-22 Max Staats , Matthias Thamm , Bernd Rosenow

Noise is the defining feature of the NISQ era, but it remains unclear if noisy quantum devices are capable of quantum speedups. Quantum supremacy experiments have been a major step forward, but gaps remain between the theory behind these…

Quantum Physics · Physics 2022-03-08 Adam Bouland , Bill Fefferman , Zeph Landau , Yunchao Liu

We show that the deterministic decision tree complexity of a (partial) function or relation $f$ lifts to the deterministic parity decision tree (PDT) size complexity of the composed function/relation $f \circ g$ as long as the gadget $g$…

Computational Complexity · Computer Science 2023-10-19 Arkadev Chattopadhyay , Nikhil S. Mande , Swagato Sanyal , Suhail Sherif

Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems,…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-06-29 Ofer Feinerman , Bernhard Haeupler , Amos Korman

In this paper, we study the quantity of computational resources (state machine states and/or probabilistic transition precision) needed to solve specific problems in a single hop network where nodes communicate using only beeps. We begin by…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-08-18 Seth Gilbert , Calvin Newport

Probabilistic verification problems of neural networks are concerned with formally analysing the output distribution of a neural network under a probability distribution of the inputs. Examples of probabilistic verification problems include…

Machine Learning · Computer Science 2025-07-11 David Boetius , Stefan Leue , Tobias Sutter

Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness. We treat these problems as…

Statistics Theory · Mathematics 2016-02-08 Siamak Ravanbakhsh , Barnabas Poczos , Russell Greiner

A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical…

Machine Learning · Computer Science 2015-08-25 Rémi Gribonval , Rodolphe Jenatton , Francis Bach

Subset selection is a fundamental problem in combinatorial optimization, which has a wide range of applications such as influence maximization and sparse regression. The goal is to select a subset of limited size from a ground set in order…

Neural and Evolutionary Computing · Computer Science 2026-04-22 Yiheng Xu , Danxuan Liu , Bin Zhang , Weiyong Yang , Chao Qian

Statistical mechanics is applied to lossy compression using multilayer perceptrons for unbiased Boolean messages. We utilize a tree-like committee machine (committee tree) and tree-like parity machine (parity tree) whose transfer functions…

Statistical Mechanics · Physics 2007-05-23 Kazushi Mimura , Masato Okada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic…

The use of parity-check gates in information theory has proved to be very efficient. In particular, error correcting codes based on parity checks over low-density graphs show excellent performances. Another basic issue of information…

Disordered Systems and Neural Networks · Physics 2007-05-23 S. Ciliberti , M. Mezard , R. Zecchina

It is known that evolution strategies in continuous domains might not converge in the presence of noise. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive…

Optimization and Control · Mathematics 2014-04-10 Marie-Liesse Cauwet