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Publications associées (339)

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Martin Jaggi, Sebastian Urban Stich, Quentin Rebjock, Sai Praneeth Reddy Karimireddy

Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient compression technique to alleviate the communication bottleneck in training large neural networks across multiple workers. We show simple convex counter-examples where signSGD doe ...

2019

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-123-FP-CIT indices

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

ObjectivesTo provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative I-123-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis.Methods(123)I-FP-CIT SPECT was performed at a singl ...

Springer2019

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

Sparse and Low-rank Modeling for Automatic Speech Recognition

Pranay Dighe

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that ...

EPFL2019

A Sparsity Measure For Echo Density Growth In General Environments

Helena Peic Tukuljac

We study the detailed temporal evolution of echo density in impulse responses for applications in acoustic analysis and rendering on general environments. For this purpose, we propose a smooth sorted density measure that yields an intuitive trend of echo d ...

IEEE2019

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019

Full-Gradient Representation for Neural Network Visualization

François Fleuret, Suraj Srinivas

We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key p ...

2019

An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis

Maryam Kamgarpour, Luca Furieri

This letter proposes a novel input-output parametrization of the set of internally stabilizing output-feedback controllers for linear time invariant (LTI) systems. Our underlying idea is to directly treat the closed-loop transfer matrices from disturbances ...

2019

SPOKEN LANGUAGE IDENTIFICATION USING LANGUAGE BOTTLENECK FEATURES

Petr Motlicek, Wissem Allouchi

In this paper, we introduce a novel approach for Language Identification (LID). Two commonly used state-of-the-art methods based on UBM/GMM I-vector technique, combined with a back-end classifier, are first evaluated. The differential factor between these ...

Idiap2019

Modeling Facial Geometry using Compositional VAEs

Pascal Fua, Timur Bagautdinov, Chenglei Wu

We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more l ...

2018

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