On the relation between linearity-generating processes and linear-rational models

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Publications associées (26)

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In recent years, the idea that the prediction of sensory input is one of the major computational goals of the nervous system led to the development of several large-scale theories of brain functioning, such as different versions of the Bayesian approach to ...

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

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Physics-Inspired Structural Representations for Molecules and Materials

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The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

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Enhancing discrete choice models with representation learning

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In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...

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Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

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Principal component analysis (PCA) finds the best linear representation of data and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "e ...

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Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogene ...

IEEE COMPUTER SOC2020

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We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

On the relevance of quality score metadata in genomic sequence data for omics applications

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High-throughput sequencing of DNA molecules has revolutionized biomedical research by enabling the quantitative analysis of the genome to study its function, structure and dynamics. It is driving sequencing-based experiments in life sciences as evidenced b ...

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Rendering-dependent compression and quality evaluation for light field contents

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Light field representation promises to overcome the limitations of stereoscopic representation by allowing for a more seamless transition between multiple points of views, thus giving a more faithful representation of 3D scenes. However, in order to take f ...

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Adversarial Analytics

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Adversarial learning is an emergent technique that provides better security to machine learning systems by deliberately protecting them against specific vulnerabilities of the learning algorithms. Many adversarial learning problems can be cast equivalently ...

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