4 citations · 7 across the 3 of their papers we have counts for
6 papers
Semi-Supervised Learning with IPM-based GANs: an Empirical Study
Tom Sercu, Youssef Mroueh
We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…
Fisher GAN
Youssef Mroueh, Tom Sercu
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore differe…
English Conversational Telephone Speech Recognition by Humans and Machines
George Saon, Gakuto Kurata, Tom Sercu +9
One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major sp…
The IBM 2016 English Conversational Telephone Speech Recognition System
George Saon, Tom Sercu, Steven Rennie +1
We describe a collection of acoustic and language modeling techniques that lowered the word error rate of our English conversational telephone LVCSR system to a record 6.6% on the…
Advances in Very Deep Convolutional Neural Networks for LVCSR
Tom Sercu, Vaibhava Goel
Very deep CNNs with small 3x3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we i…