Concept

Connectionism

Related publications (54)

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We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

IEEE2018

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

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Multilingual models for Automatic Speech Recognition (ASR) are attractive as they have been shown to benefit from more training data, and better lend themselves to adaptation to under-resourced languages. However, initialisation from monolingual context-de ...

ELSEVIER SCIENCE BV2018

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The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...

2017

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Different training and adaptation techniques for multilingual Automatic Speech Recognition (ASR) are explored in the context of hybrid systems, exploiting Deep Neural Networks (DNN) and Hidden Markov Models (HMM). In multilingual DNN training, the hidden l ...

2017

Question Answering in Conversations: Query Refinement Using Contextual and Semantic Information

Andrei Popescu-Belis, Maryam Habibi, Parvaz Mahdabi

This paper introduces a query refinement method applied to questions asked by users to a system during a meeting or a conversation that they have with other users. To answer the questions, the proposed method leverages the local context of the conversation ...

Idiap2016

Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques

Pascal Fua, Horesh Beny Ben Shitrit

This paper proposes a relational learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is ...

2016

Models of crowding: a comparative study

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Aaron Michael Clarke

In crowding, the perception of an object deteriorates in the presence of nearby elements. Obviously, crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation. Despite this ubiquity, there exists no consensus on how to model crowding ...

2016

Improving Object Classification using Pose Information

François Fleuret, Hugo Augusto Penedones Fernandes, Ronan Collobert, David Grangier

We propose a method that exploits pose information in order to improve object classification. A lot of research has focused in other strategies, such as engineering feature extractors, trying different classifiers and even using transfer learning. Here, we ...

Idiap2012