Publications related to A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...

ACM2018

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

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

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

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

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M

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2015

Temporal Analysis of Motif Mixtures using Dirichlet Processes

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

In this article, we present a new model for unsupervised discovery of recurrent temporal patterns (or motifs) in time series (or documents). The model is designed to handle the difficult case of multivariate time series obtained from a mixture of activitie ...

2014

Integrating psychometric indicators in latent class choice models

Michel Bierlaire, Ricardo Daniel Hurtubia González, Aurélie Glerum, My Hang Nguyen

Latent class models are a convenient and intuitive way to introduce taste heterogeneity in discrete choice models by relating attributes of the decision makers with unobserved behavioral classes, hence allowing for a more accurate market segmentation. Esti ...

Pergamon-Elsevier Science Ltd2014

Using semi-open questions to integrate perceptions in choice models

Michel Bierlaire, Aurélie Glerum, Bilge Atasoy

This research investigates the measurement of perceptions by means of adjectives freely reported by respondents in semi-open questions. It involved the use of semi-open responses of 1'763 Swiss individuals to develop indicators for a latent variable repres ...

Elsevier Sci Ltd2014

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

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Enhancing Session-Based Recommendations through Sequential Modeling

Applications of Approximate Learning and Inference for Probabilistic Models

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Data Summarization with Social Contexts

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Temporal Analysis of Motif Mixtures using Dirichlet Processes

Integrating psychometric indicators in latent class choice models

Using semi-open questions to integrate perceptions in choice models

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Data Summarization with Social Contexts

Temporal Analysis of Motif Mixtures using Dirichlet Processes

Enhancing Session-Based Recommendations through Sequential Modeling

Applications of Approximate Learning and Inference for Probabilistic Models

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Using semi-open questions to integrate perceptions in choice models

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Integrating psychometric indicators in latent class choice models

Multi-Modal Mean-Fields via Cardinality-Based Clamping