Gilles Wainrib

发表

Scaling Up Echo-State Networks With Multiple Light Scattering

Jonathan Dong, Florent Krzakala, Sylvain Gigan, 2016, 2018 IEEE Statistical Signal Processing Workshop (SSP).

Perspectives en matrices aléatoires et grands réseaux

Romain Couillet, Gilles Wainrib, Romain Couillet, 2016, Traitement du Signal.

Some Numerical Methods for Rare Events Simulation and Analysis

Gilles Wainrib, G. Wainrib, 2013 .

A local Echo State Property through the largest Lyapunov exponent

Gilles Wainrib, Mathieu Galtier, 2016, Neural Networks.

Robust Detection of Covariate-Treatment Interactions in Clinical Trials

Eric W. Tramel, Gilles Wainrib, Baptiste Goujaud, 2017 .

Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach pdf

Eric W. Tramel, Gilles Wainrib, Pierre Courtiol, 2018, ArXiv.

Reduction of stochastic conductance-based neuron models with time-scales separation

Khashayar Pakdaman, Gilles Wainrib, Michèle Thieullen, 2011, Journal of Computational Neuroscience.

Random walks on binary strings applied to the somatic hypermutation of B-cells.

Gilles Wainrib, Irene Balelli, Vuk Milišić, 2018, Mathematical biosciences.

New Results - Macroscopic limits of stochastic neural networks and neural fields

Jonathan Touboul, Gilles Wainrib, Alberto Romagnoni, 2015 .

The Asymptotic Performance of Linear Echo State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, 2016, J. Mach. Learn. Res..

ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors pdf

Eric W. Tramel, Gilles Wainrib, Mikhail Zaslavskiy, 2018, Computational Toxicology.

Abstract 2105: HE2RNA: A deep learning model for transcriptomic learning from digital pathology

Gilles Wainrib, Pierre Courtiol, Mikhail Zaslavskiy, 2020 .

A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity

Gilles Wainrib, Mathieu Galtier, G. Wainrib, 2012, Neural Computation.

Erratum to: Intrinsic variability of latency to first-spike

Khashayar Pakdaman, Gilles Wainrib, Michèle Thieullen, 2011, Biological Cybernetics.

Training performance of echo state neural networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, 2016, 2016 IEEE Statistical Signal Processing Workshop (SSP).

A Random Matrix Approach to Echo-State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, 2016, ICML.

Intrinsic variability of latency to first-spike

Khashayar Pakdaman, Gilles Wainrib, Michèle Thieullen, 2010, Biol. Cybern..

A combination of STDP, Hebbian learning and synaptic scaling deriving from a theoretical learning principle pdf

Gilles Wainrib, Mathieu Galtier, 2012, ArXiv.

Distributed synaptic weights in a LIF neural network and learning rules

Gilles Wainrib, Benoit Perthame, Delphine Salort, 2017 .

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Stanislas Chambon, Gilles Wainrib, Alexandre Gramfort, 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Multiscale analysis of slow-fast neuronal learning models with noise

Gilles Wainrib, Mathieu Galtier, G. Wainrib, 2012, Journal of mathematical neuroscience.

Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes

Herbert Jaeger, Gilles Wainrib, Mathieu Galtier, 2014, Neural Networks.

Synchronization in random balanced networks.

Jonathan Touboul, Khashayar Pakdaman, Gilles Wainrib, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

Limit theorems for infinite-dimensional piecewise deterministic Markov processes. Applications to stochastic excitable membrane models

Gilles Wainrib, G. Wainrib, Martin G. Riedler, 2011, 1112.4069.

A Brief Introduction to Large Deviations Theory

Gilles Wainrib, G. Wainrib, 2013 .

Double averaging principle for periodically forced slow-fast stochastic systems

Gilles Wainrib, 2013 .

Asymptotic expansion and central limit theorem for multiscale piecewise-deterministic Markov processes

Khashayar Pakdaman, Gilles Wainrib, Michèle Thieullen, 2012 .

Optimal system size for complex dynamics in random neural networks near criticality.

Gilles Wainrib, G. Wainrib, L. C. Garcia Del Molino, 2013, Chaos.

Diffusion approximation of birth-death processes: Comparison in terms of large deviations and exit points

Khashayar Pakdaman, Gilles Wainrib, Michèle Thieullen, 2010 .

Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis

Gilles Wainrib, Ian Morilla, G. Wainrib, 2019, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

Mathematical modeling of lymphocytes selection in the germinal center

Gilles Wainrib, Vuk Milisic, 2017, Journal of mathematical biology.

Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides

Oumeima Laifa, Gilles Wainrib, Jean-Michel Pawlotsky, 2020, Hepatology.