activity
20182022
most citedMulti-objective training of Generative Adversarial Networks with multiple discriminators

25 citations · 25 across the 4 of their papers we have counts for

collaborators

10 papers

cs.LG2022

Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification

Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi +1

We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity. Specifically, we…

cs.LG2021

Domain Conditional Predictors for Domain Adaptation

Joao Monteiro, Xavier Gibert, Jianqiao Feng +2

Learning guarantees often rely on assumptions of i.i.d. data, which will likely be violated in practice once predictors are deployed to perform real-world tasks. Domain adaptation…

cs.LG2020

An end-to-end approach for the verification problem: learning the right distance

Joao Monteiro, Isabela Albuquerque, Jahangir Alam +2

In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn f…

eess.AS2020

Multi-task self-supervised learning for Robust Speech Recognition

Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual +4

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recentl…

cs.CL2019

A Simplified Fully Quantized Transformer for End-to-end Speech Recognition

Alex Bie, Bharat Venkitesh, Joao Monteiro +2

While significant improvements have been made in recent years in terms of end-to-end automatic speech recognition (ASR) performance, such improvements were obtained through the use…

cs.LG201925 cited

Multi-objective training of Generative Adversarial Networks with multiple discriminators

Isabela Albuquerque, João Monteiro, Thang Doan +3

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involvin…