Search Results for author: Mina Khan

Found 5 papers, 2 papers with code

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning

Pretrained Encoders are All You Need

1 code implementation ICML Workshop URL 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Rishabh Anand, Sherjil Ozair, Pattie Maes

Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-label datasets.

Contrastive Learning reinforcement-learning +1

Personalizing Pre-trained Models

no code implementations2 Jun 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Asadali Hazariwala, Pattie Maes

Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings.

Continual Learning Few-Shot Learning +2

PAL: Intelligence Augmentation using Egocentric Visual Context Detection

no code implementations22 May 2021 Mina Khan, Pattie Maes

We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection.

Face Detection Privacy Preserving

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