R Devon Hjelm

Data-Efficient Reinforcement Learning with Self-Predictive Representations

2020 · arXiv (Cornell University)

L'efficacité des données reste un défi majeur dans l'apprentissage par renforcement profond. Bien que les techniques modernes soient capables d'atteindre des performances élevées dans des tâches extrêmement complexes, y compris les jeux de stratégie comme …

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

2020 · arXiv (Cornell University)

In this contribution, we augment the metric learning setting by introducing a\nparametric pseudo-distance, trained jointly with the encoder. Several\ninterpretations are thus drawn for the learned distance-like model's output. We\nfirst show it approximates a likelihood ratio …

Deep Graph Infomax

2018 · Apollo (University of Cambridge)

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both …