Publications associées à Connexionnisme

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

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

Cross-lingual Adaptation of a CTC-based multilingual Acoustic Model

Hervé Bourlard, Philip Neil Garner, Sibo Tong

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

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Martin Jaggi, Matteo Pagliardini, Prakhar Gupta

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

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

Hervé Bourlard, Philip Neil Garner, Sibo Tong

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

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

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Connexionnisme

Learning to Find Good Correspondences

Learning to Find Good Correspondences

Cross-lingual Adaptation of a CTC-based multilingual Acoustic Model

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

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

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

Models of crowding: a comparative study

Improving Object Classification using Pose Information

Graph Chatbot

Chattez avec Graph Search

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

Learning to Find Good Correspondences

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Models of crowding: a comparative study

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

Improving Object Classification using Pose Information

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

Cross-lingual Adaptation of a CTC-based multilingual Acoustic Model

Learning to Find Good Correspondences