Concept

Long short-term memory

Publications associées (143)

Uncorking the bottleneck of crowding: a fresh look at object recognition

In crowding, the perception of a target deteriorates in the presence of clutter. Crowding is usually explained within the framework of object recognition, where processing proceeds in a hierarchical and feedforward fashion from the analysis of low level fe ...

2015

Human and Machine Learning in Non-Markovian Decision Making

Michael Herzog, Aaron Michael Clarke, Elisa Tartaglia, Silvia Marchesotti, Walter Senn

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Ma ...

Public Library of Science2015

Prediction of Asynchronous Dimensional Emotion Ratings from Audiovisual and Physiological Data

Jean-Philippe Thiran, Touradj Ebrahimi, Eleni Kroupi, Anil Yuce

Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models. Whereas the dimensional approach is getting more and more popular for rating: affective behaviours in con ...

Elsevier2015

Speaker diarization of spontaneous meeting room conversations

Sree Harsha Yella

Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization syste ...

EPFL2015

Speaker diarization of spontaneous meeting room conversations

Sree Harsha Yella

Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization sys ...

EPFL2015

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Pierre Vandergheynst, Michael Bronstein

In this paper, we propose a generalization of convolutional neural networks (CNN) to non-Euclidean domains for the analysis of deformable shapes. Our construction is based on localized frequency analysis (a generalization of the windowed Fourier transform ...

Wiley-Blackwell2015

Joint Phoneme Segmentation Inference and Classification using CRFs

Ronan Collobert, Dimitri Palaz

State-of-the-art phoneme sequence recognition systems are based on hybrid hidden Markov model/artificial neural networks (HMM/ANN) framework. In this framework, the local classifier, ANN, is typically trained using Viterbi expectation-maximization algorith ...

2014

FE modeling of micromotion at the bone-implant interface

Denis Stauffer

Nowadays, pain and mobility issues of the hip such as arthritis bring more and more people to go through a hip replacement. The primary stability of an implant is essential for the long-term success of hip arthroplasty. Excessive micromotion is an indicato ...

2014

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

Ronan Collobert, Dimitri Palaz

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...

2013

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

Ronan Collobert, Dimitri Palaz

Idiap2013

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