515 citations · 1.2k across the 10 of their papers we have counts for
20 papers
Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver
Levent Sagun, Caglar Gulcehre, Adriana Romero +2
Science meets Engineering in Deep Learning took place in Vancouver as part of the Workshop section of NeurIPS 2019. As organizers of the workshop, we created the following report i…
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown brea…
Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu, Caglar Gulcehre, Tom Le Paine +2
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time. However, their saturation property introd…
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Tom Le Paine, Caglar Gulcehre, Bobak Shahriari +11
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial…
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes +5
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over…
Sample Efficient Adaptive Text-to-Speech
Yutian Chen, Yannis Assael, Brendan Shillingford +11
We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and ind…