Search Results for author: Cuong Nguyen

Found 6 papers, 3 papers with code

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

no code implementations10 Aug 2021 Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram, Chao Li, Chuan Sheng Foo, Pavitra Krishnaswamy

Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data.

Classification Image Classification +1

Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories

1 code implementation28 Apr 2021 Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes

We develop an Approximate Bayesian Computation approach that draws samples from the posterior distribution over the model's transition and duration parameters given aggregate counts from a specific location, thus adapting the model to a region or individual hospital site of interest.

Similarity of Classification Tasks

1 code implementation27 Jan 2021 Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro

Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks.

Classification Few-Shot Learning +1

PAC-Bayesian Meta-learning with Implicit Prior and Posterior

no code implementations5 Mar 2020 Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro

We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a prior distribution of the model of interest.

Classification Few-Shot Image Classification +1

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

1 code implementation27 Jul 2019 Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning.

Classification Few-Shot Image Classification +2

Unsupervised Task Design to Meta-Train Medical Image Classifiers

no code implementations17 Jul 2019 Gabriel Maicas, Cuong Nguyen, Farbod Motlagh, Jacinto C. Nascimento, Gustavo Carneiro

Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i. e., classifiers modeled with small training sets).

Classification Few-Shot Learning +1

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