21 citations · 41 across the 6 of their papers we have counts for
6 papers
Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons
Sidi Yaya Arnaud Yarga, Sean U. N. Wood
Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware. The most widely used spiking neuron in deep le…
Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars
Philippe Drolet, Raphaël Dawant, Victor Yon +9
Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1…
Efficient spike encoding algorithms for neuromorphic speech recognition
Sidi Yaya Arnaud Yarga, Jean Rouat, Sean U. N. Wood
Spiking Neural Networks (SNN) are known to be very effective for neuromorphic processor implementations, achieving orders of magnitude improvements in energy efficiency and computa…
Robust learning from corrupted EEG with dynamic spatial filtering
Hubert Banville, Sean U. N. Wood, Chris Aimone +2
Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly…
Evaluation of a Vision-to-Audition Substitution System that Provides 2D WHERE Information and Fast User Learning
Louis Commère, Sean U. N. Wood, Jean Rouat
Vision to audition substitution devices are designed to convey visual information through auditory input. The acceptance of such systems depends heavily on their ease of use, train…
Unsupervised Low Latency Speech Enhancement with RT-GCC-NMF
Sean U. N. Wood, Jean Rouat
In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning…