Multi-Stage Transfer Learning with an Application to Selection Process

no code implementations • 1 Jun 2020 • Andre Mendes, Julian Togelius, Leandro dos Santos Coelho

In this work, we proposed a \textit{Multi-StaGe Transfer Learning} (MSGTL) approach that uses knowledge from simple classifiers trained in early stages to improve the performance of classifiers in the latter stages.

Transfer Learning

Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes

no code implementations • 15 Mar 2020 • Andre Mendes, Julian Togelius, Leandro dos Santos Coelho

We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions.

Medical Diagnosis Multi-Task Learning

Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder

no code implementations • 15 Mar 2020 • Andre Mendes, Julian Togelius, Leandro dos Santos Coelho

We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains.

Imputation Multi-Task Learning