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Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian…

Machine Learning · Computer Science 2016-09-22 P. K. Srijith , P. Balamurugan , Shirish Shevade

We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential…

Machine Learning · Computer Science 2019-12-10 Mikhail Khodak , Maria-Florina Balcan , Ameet Talwalkar

We extend in a natural way the operation of Turing machines to infinite ordinal time, and investigate the resulting supertask theory of computability and decidability on the reals. The resulting computability theory leads to a notion of…

Logic · Mathematics 2007-05-23 Joel David Hamkins , Andy Lewis

Projection algorithms are well known for their simplicity and flexibility in solving feasibility problems. They are particularly important in practice due to minimal requirements for software implementation and maintenance. In this work, we…

Optimization and Control · Mathematics 2020-04-14 Minh N. Dao , Hung M. Phan

Optimization problems, generalized equations, and the multitude of other variational problems invariably lead to the analysis of sets and set-valued mappings as well as their approximations. We review the central concept of set-convergence…

Optimization and Control · Mathematics 2020-02-25 Johannes O. Royset

In this paper we have investigated enumeration orders of elements of r.e. sets enumerated by means of Turing machines. We have defined a reducibility based on enumeration orders named "Enumeration Order Reducibility" on computable functions…

Logic in Computer Science · Computer Science 2010-06-28 Ali Akbar Safilian , Farzad Didehvar

The first part of this article deals with theorems on uniqueness in law for \sigma-finite and constructive countable random sets, which in contrast to the usual assumptions may have points of accumulation. We discuss and compare two…

Probability · Mathematics 2012-07-24 Philip Herriger

People can learn a new concept and use it compositionally, understanding how to "blicket twice" after learning how to "blicket." In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality,…

Computation and Language · Computer Science 2019-10-10 Brenden M. Lake

In this paper we study structural properties of LV-degrees of the algebra of collections of sequences that are non-negligible in the sense that they can be computed by a probabilistic algorithm with positive probability. We construct atoms…

Information Theory · Computer Science 2021-09-10 Vladimir V. V'yugin

Generalized semi-infinite programs (GSIP) are a class of mathematical optimization problems that generalize semi-infinite programs, which have a finite number of decision variables and infinite constraints. Mitsos et al. (Mitsos and…

Optimization and Control · Mathematics 2019-12-04 Stuart M. Harwood

A definition of summability is put forward in the framework of general Carleman ultraholomorphic classes in sectors, so generalizing $k-$summability theory as developed by J.-P. Ramis. Departing from a strongly regular sequence of positive…

Complex Variables · Mathematics 2014-02-10 Alberto Lastra , Stephane Malek , Javier Sanz

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally…

Machine Learning · Computer Science 2023-06-27 Muning Wen , Runji Lin , Hanjing Wang , Yaodong Yang , Ying Wen , Luo Mai , Jun Wang , Haifeng Zhang , Weinan Zhang

Recently, Gavazzo has developed a relational theory of symbolic manipulation, that allows to study syntax-based rewriting systems without relying on specific notions of syntax. This theory was obtained by extending the algebra of relations…

Logic in Computer Science · Computer Science 2023-12-07 Lorenzo Pace

BSS RAMs over first-order structures help to characterize algorithms for processing objects by means of useful operations and relations. They are the result of a generalization of several types of abstract machines. We want to discuss…

Logic · Mathematics 2025-05-01 Christine Gaßner

We present a uniform theory of constructible sheaves on arbitrary schemes with coefficients in topological or even condensed rings. This is accomplished by defining lisse sheaves to be the dualizable objects in the derived infinity-category…

Algebraic Geometry · Mathematics 2023-05-30 Tamir Hemo , Timo Richarz , Jakob Scholbach

Sesqui-pushout (SqPO) rewriting is a variant of transformations of graph-like and other types of structures that fit into the framework of adhesive categories where deletion in unknown context may be implemented. We provide the first…

Logic in Computer Science · Computer Science 2019-12-23 Nicolas Behr

The aim of this note is threefold. The first is to obtain a simple characterization of relative constructible sheaves when the parameter space is projective. The second is to study the relative Fourier-Mukai for relative constructible…

Algebraic Geometry · Mathematics 2025-08-19 Luisa Fiorot , Teresa Monteiro Fernandes

The purpose of this contribution is to give a coherent account of a particular narrative which links locales, geometric theories, sheaf semantics and constructive commutative algebra. We are hoping to convey a firm grasp of three ideas: (1)…

Logic · Mathematics 2020-12-29 Ingo Blechschmidt

We introduce a notion of realizability with ordinal Turing machines based on recognizability rather than computability, i.e., the ability to uniquely identify an object. We show that the arising concept of $r$-realizabilty has the property…

Logic · Mathematics 2024-08-14 Merlin Carl

A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all…

Machine Learning · Computer Science 2019-05-09 Michael B. Chang , Abhishek Gupta , Sergey Levine , Thomas L. Griffiths
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