Analyse sémantique latente probabiliste

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Publications associées (29)

Lancet: A self-correcting Latency Measuring Tool

Edouard Bugnion, Evangelos Marios Kogias, Stephen Xin Mallon

We present LANCET, a self-correcting tool designed to measure the open-loop tail latency of µs-scale datacenter applications with high fan-in connection patterns. LANCET is self-correcting as it relies on online statistical tests to determine situations in ...

USENIX ASSOC2019

Motility as a tool to uncover mobility practices

Vincent Kaufmann, Guillaume Simon Joseph Drevon, Alexis Gumy, Eloi Antoine Maël Bernier

This paper investigate the causal relationships that exist between the motility, defined as the personal ability to be mobile (Kaufmann et al., 2015), and mobility behaviors. To do so, we use the first wave (2018) of the "national daily mobility panel" sur ...

2019

Transitions of Tonality: A Model-Based Corpus Study

Fabian Claude Moss

Tonality has been the cornerstone of Western music-theoretical discourse for centuries. This study addresses the subject, using traditional music analysis, data-driven corpus methods, and computational models, concentrating on historical changes of tonalit ...

EPFL2019

Joint Learning of Semantic Alignment and Object Landmark Detection

Seungryong Kim

Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly ...

IEEE2019

Text Similarity in Vector Space Models: A Comparative Study

Kenneth Younge, Omid Shahmirzadi, Adam Lugowski

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to ...

2018

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Mitigating the impact of errors in travel time reporting on mode choice modelling

Michel Bierlaire, Aurélie Glerum, Amanda Blomberg Stathopoulos, Giovanni Longo, Silvia Francesca Varotto

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper fo ...

Elsevier Sci Ltd2017

Data Summarization with Social Contexts

Karl Aberer, Tian Guo, Rameez Rahman, Hao Zhuang, Xia Hu

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summariza ...

Assoc Computing Machinery2016

Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Anna Fernández Antolín

In this paper we extend the Multiple Indicator Solution (MIS) so that it can also be used to account for endogeneity when there are interactions between observed and unobserved factors in the specification of the utility function. We develop the theoretica ...

Elsevier Sci Ltd2016

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