Robustness, replicability and scalability in topic modelling

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Music analysis, in particular harmonic analysis, is concerned with the way pitches are organized in pieces of music, and a range of empirical applications have been developed, for example, for chord recognition or key finding. Naturally, these approaches r ...

Ever since the links between the development of new technologies and economic growth became evident, researchers have attempted to study how the creation of knowledge fosters progress. If pushing the frontier of knowledge has an impact on progress and well ...

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

Methods of estimating the similarity between individual publications is an area of long-standing interest in the scientometrics community. Traditional methods have generally relied on references and other metadata, while text mining approaches based on tit ...

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 ...

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

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 ...

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 ...

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

Adapting statistical learning models online with large scale streaming data is a challenging problem. Bayesian non-parametric mixture models provide flexibility in model selection, however, their widespread use is limited by the computational overhead of e ...

Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in captu ...

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ...

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequenti ...

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 ...

As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuo ...

With the increased presence of digital imaging devices there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators and have started organizing themselves in ...

An information-geometric approach for document similarities in the framework of “Probabilistic Latent Semantic Indexing” was ﬁrst proposed by T. Hofmann (2000) and later extended (“revisited”) by Nyffenegger et al. (2006). This paper presents an in-depth s ...

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

Robustness, replicability and scalability in topic modelling

Chattez avec Graph Search

Discovering Tonal Profiles with Latent Dirichlet Allocation

The organisation of science: topics, incentives and funding

Multi-scale sequential network for semantic text segmentation and localization

Evolution of Topics and Novelty in Science

Text Similarity in Vector Space Models: A Comparative Study

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Applications of Approximate Learning and Inference for Probabilistic Models

Data Summarization with Social Contexts

Word Embeddings for Natural Language Processing

Online Inference in Bayesian Non-Parametric Mixture Models under Small Variance Asymptotics

Rehabilitation of Count-based Models for Word Vector Representations

Similarity Learning Over Large Collaborative Networks

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

Sequential Topic Models for Mining Recurrent Activities and their Relationships : Application to long term video recordings

A Probabilistic Approach to Socio-Geographic Reality Mining

Modeling and Understanding Flickr Communities through Topic-based Analysis

Rôle de la matrice d'information et pondération des composantes dans les noyaux de Fisher pour PLSI

Machine learning for information retrieval