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Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes…

Machine Learning · Computer Science 2012-07-03 Konstantina Palla , David Knowles , Zoubin Ghahramani

Gauging and duality transformations, two of the most useful tools in many-body physics, are shown to be equivalent up to constant depth quantum circuits in the case of one-dimensional quantum lattice models. This is demonstrated by making…

We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints…

Machine Learning · Statistics 2017-09-21 Seungil You , David Ding , Kevin Canini , Jan Pfeifer , Maya Gupta

We review the notion of symplectic duality earlier introduced in the context of topological recursion. We show that the transformation of symplectic duality can be expressed as a composition of $x-y$ dualities in a broader context of log…

Mathematical Physics · Physics 2024-12-05 Alexander Alexandrov , Boris Bychkov , Petr Dunin-Barkowski , Maxim Kazarian , Sergey Shadrin

We develop a system-theoretic framework for the structured analysis of distributed optimization algorithms with decomposable cost functions. We model such algorithms as a network of interacting dynamical systems and derive tests for…

Optimization and Control · Mathematics 2026-04-14 Aron Karakai , Jaap Eising , Andrea Martinelli , Florian Dörfler

In this paper, a subclass of bounded distributive lattices, that is, finitely disjunctive distributive lattices (FDD-lattices) have been introduced. Then we apply it to establish a Stone duality for Lawson compact algebraic L-domains.…

General Topology · Mathematics 2026-02-16 Huijun Hou , Ao Shen

We establish a consistency result by comparing two independent notions of generalised solutions to a large class of linear hyperbolic first order PDE systems with constant coefficients, showing that they eventually coincide. The first is…

Analysis of PDEs · Mathematics 2018-01-25 Nikos Katzourakis

Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms. In this context, much…

Machine Learning · Computer Science 2023-02-10 Danilo Numeroso , Davide Bacciu , Petar Veličković

Motivated by questions in biological classification, we discuss some elementary combinatorial and computational properties of certain set systems that generalize hierarchies, namely, 'patchworks', 'weak patchworks', 'ample patchworks' and…

Populations and Evolution · Quantitative Biology 2012-02-16 Andreas Dress , Vincent Moulton , Mike Steel , Taoyang Wu

We introduce a general framework for generating dualities between categories of partial orders and categories of ordered Stone spaces; we recover in particular the classical Priestley duality for distributive lattices and establish several…

Category Theory · Mathematics 2012-03-14 Olivia Caramello

The analysis of complex networks has so far revolved mainly around the role of nodes and communities of nodes. However, the dynamics of interconnected systems is commonly focalised on edge processes, and a dual edge-centric perspective can…

Physics and Society · Physics 2014-04-25 Michael T. Schaub , Jörg Lehmann , Sophia N. Yaliraki , Mauricio Barahona

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. It arises in various application domains,…

Optimization and Control · Mathematics 2015-03-17 John Duchi , Alekh Agarwal , Martin Wainwright

Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how…

Machine Learning · Computer Science 2017-09-19 Théo Trouillon , Éric Gaussier , Christopher R. Dance , Guillaume Bouchard

We develop dualities for complete perfect distributive quasi relation algebras and complete perfect distributive involutive FL-algebras. The duals are partially ordered frames with additional structure. These frames are analogous to the…

Logic in Computer Science · Computer Science 2026-01-30 Andrew Craig , Peter Jipsen , Claudette Robinson

A recent line of work in NLP focuses on the (dis)ability of models to generalise compositionally for artificial languages. However, when considering natural language tasks, the data involved is not strictly, or locally, compositional.…

Computation and Language · Computer Science 2023-02-01 Verna Dankers , Ivan Titov

Modern computer networks support interesting new routing models in which traffic flows from a source s to a destination t can be flexibly steered through a sequence of waypoints, such as (hardware) middleboxes or (virtualized) network…

Networking and Internet Architecture · Computer Science 2017-09-04 Saeed Akhoondian Amiri , Klaus-Tycho Foerster , Riko Jacob , Stefan Schmid

Motivated by Gentzen disjunction elimination rule in his Natural Deduction calculus and reading inequalities with meet in a natural way, we conceive a notion of distributivity for join-semilattices. We prove that it is equivalent to a…

Logic · Mathematics 2019-02-06 Rodolfo C. Ertola-Biraben , Francesc Esteva , Lluís Godo

Using Sheaf duality theory of Comer for cylindric algebras, we give a representation theorem of of distributive bounded lattices expanded by modalities (functions distributing over joins) as the continuous sections of sheaves. Our…

Logic · Mathematics 2013-04-03 Tarek Sayed Ahmed

We investigate the duality structure of quantum lattice systems with topological order, a collective order also appearing in fractional quantum Hall systems. We define electromagnetic (EM) duality for all of Kitaev's quantum double models…

Strongly Correlated Electrons · Physics 2013-10-09 Oliver Buerschaper , Matthias Christandl , Liang Kong , Miguel Aguado

Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning. Though the original IB has been extensively studied, there has not been much…

Machine Learning · Computer Science 2019-10-08 Thanh T. Nguyen , Jaesik Choi
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