Search Results for author: Isabela Albuquerque

Found 11 papers, 7 papers with code

Learning Semantic Similarities for Prototypical Classifiers

no code implementations1 Jan 2021 Joao Monteiro, Isabela Albuquerque, Jahangir Alam, Tiago Falk

Recent metric learning approaches parametrize semantic similarity measures through the use of an encoder trained along with a similarity model, which operates over pairs of representations.

Few-Shot Learning Metric Learning +4

Improving out-of-distribution generalization via multi-task self-supervised pretraining

no code implementations30 Mar 2020 Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Keskar, Richard Socher

Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness.

Adversarial Robustness Computer Vision +5

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

1 code implementation ICML 2020 Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago Falk

In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder.

Metric Learning

Self-supervised representation learning from electroencephalography signals

1 code implementation13 Nov 2019 Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort

The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.

Computer Vision Representation Learning +2

Generalizing to unseen domains via distribution matching

1 code implementation3 Nov 2019 Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas

In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).

Domain Generalization Object Recognition +2

Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment

no code implementations20 Jun 2019 Isabela Albuquerque, João Monteiro, Olivier Rosanne, Abhishek Tiwari, Jean-François Gagnon, Tiago H. Falk

Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.

Domain Adaptation EEG

Multi-objective training of Generative Adversarial Networks with multiple discriminators

1 code implementation ICLR 2019 Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.

Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation

1 code implementation23 Jan 2019 Isabela Albuquerque, João Monteiro, Tiago H. Falk

Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.

Video Generation

Deep learning-based electroencephalography analysis: a systematic review

3 code implementations16 Jan 2019 Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H. Falk, Jocelyn Faubert

To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.

Brain Decoding EEG +1

On-line Adaptative Curriculum Learning for GANs

3 code implementations31 Jul 2018 Thang Doan, Joao Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm

We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.

Multi-Armed Bandits Stochastic Optimization

Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch

no code implementations21 Feb 2018 João Monteiro, Isabela Albuquerque, Zahid Akhtar, Tiago H. Falk

Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.

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