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Analyzing Flickr Groups

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Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

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NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Extremal attractors of Liouville copulas

Liouville copulas introduced in McNeil and Ne lehova (2010) are asymmetric generalizations of the ubiquitous Archimedean copula class. They are the dependence structures of scale mixtures of Dirichlet distributions, also called Liouville distributions. In ...

2017

Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification

Jean-Marc Odobez, Pierre Moulin, Jagannadan Varadarajan

We present a novel anomaly detection (AD) system for streaming videos. Different from prior methods that rely on unsupervised learning of clip representations, that are usually coarse in nature, and batch-mode learning, we propose the combination of two no ...

2017

N-gram-Based Low-Dimensional Representation for Document Classification

Rémi Philippe Lebret, Ronan Collobert

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent D ...

2015

Two-stage online inference model for traffic pattern analysis and anomaly detection

Kwang Moo Yi

In this paper, we propose a method for modeling trajectory patterns with both regional and velocity observations through the probabilistic topic model. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocit ...

Springer Verlag2014

Statistical models for HMM/ANN hybrids

Philip Neil Garner, David Imseng

We present a theoretical investigation into the use of normalised artificial neural network (ANN) outputs in the context of hidden Markov models (HMMs). The work is motivated by the pursuit of a more theoretically rigorous understanding of the Kullback-Lie ...

Idiap2013

Sequential Topic Models for Mining Recurrent Activities and their Relationships

Jagannadan Varadarajan

In this thesis, we address the analysis of activities from long term data logs with an emphasis on video recordings. Starting from simple words from video, we progressively build methods to infer higher level scene semantics. The main strategies used to ac ...

EPFL2012

Spam Fighting in Social Tagging Systems

Touradj Ebrahimi, Sasan Yazdani, Ivan Ivanov

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of diverse resources available online. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes ...

Springer-Verlag2012

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We tackle the problem of improving the relevance of automatically selected tags in large-scale ontology-based information systems. Contrary to traditional settings where tags can be chosen arbitrarily, we focus on the problem of recommending tags (e.g., co ...

Springer2012