Romain Tavenard

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Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation(More)
Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the(More)
The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the(More)
RÉSUMÉ. Dans la plupart des applications de RI, calculer rapidement la proximité entre documents et requêtes est crucial. Avec les modèles vectoriels, ce calcul se fait généralement de manière très efficace. Cependant, lorsque les requêtes sont très longues ou dans le cas de SRI basés sur des modèles plus avancés, ce calcul devient plus complexe et coûteux.(More)
In this paper, we postulate that temporal information is important for action recognition in videos. Keeping temporal information, videos are represented as word×time documents. We propose to use time-sensitive probabilistic topic models and we extend them for the context of supervised learning. Our time-sensitive approach is compared to both PLSA(More)
Dynamic time warping (DTW) is the most popular approach for evaluating the similarity of time series, but its computation is costly. Therefore, simple functions lower bounding DTW distances have been designed, accelerating searches by quickly pruning sequences that could not possibly be best matches. The tighter the bounds, the more they prune and the(More)
Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-ofWords approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has(More)
Time series classification has been around for decades in the data-mining and machine learning communities. In this paper, we investigate the use of convolutional neural networks (CNN) for time series classification. Such networks have been widely used in many domains like computer vision and speech recognition, but only a little for time series(More)
Due to rapid advances in networking and sensing technology we are witnessing a growing interest in sensor networks, in which a variety of sensors are connected to each other and to computational devices capable of multimodal signal processing and data analysis. Such networks are seen to play an increasingly important role as key enablers in emerging(More)
SAX (Symbolic Aggregate approXimation) is one of the main symbolization technique for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that contain each an information about the average and the trend of the(More)