Concept

Group method of data handling

Publications associées (62)

Deep Neural Networks for Multiple Speaker Detection and Localization

Jean-Marc Odobez, Petr Motlicek, Weipeng He

We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require few ...

2018

A quantum-implementable neural network model

Edoardo Charbon, Lingli Wang

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this m ...

2017

Efﬁcient Feature Embeddings for Student Classiﬁcation with Variational Auto-encoders

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high ...

EDM2017

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

Davide Campi, Edoardo Baldi

Nanowires made of chalcogenide alloys are of interest for use in phase-change nonvolatile memories. For this application, insights into the thermal properties of such nanowires and, in particular, into the crystallization kinetics at the atomic level are c ...

American Chemical Society2017

Low-Rank Representation of Nearest Neighbor Phone Posterior Probabilities to Enhance DNN Acoustic Modeling

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We hypothesize that optimal deep neural networks (DNN) class-conditional posterior probabilities live in a union of low-dimensional subspaces. In real test conditions, DNN posteriors encode uncertainties which can be regarded as a superposition of unstruct ...

Idiap2016

Parallel algorithms for tensor completion in the CP format

Daniel Kressner, André Uschmajew

Low-rank tensor completion addresses the task of filling in missing entries in multidimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large-sc ...

Elsevier Science Bv2016

Preconditioned Spectral Descent for Deep Learning

Volkan Cevher, Ya-Ping Hsieh, Edo Collins

Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...

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

Adaptive Sampling for Large Scale Boosting

François Fleuret, Charles Dubout

Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such con ...

2014

Implementation of a Human Feedback-based Locomotion and its Control by means of a Feedforward Component inspired by Central Pattern Generators

Florin Dzeladini

Walking gaits of various animals have been modeled using this framework of differential equations, and more specifically, using network of coupled oscillators or CPG (Central Pattern Generators). In these models, oscillators are coupled among themselves an ...

2013

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