259 citations · 1.1k across the 54 of their papers we have counts for
8 papers · 2 filters
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio, Prateek Gupta, Tegan Maharaj +20
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-sca…
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy +1
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We inv…
Reinforcement Learning with Random Delays
Simon Ramstedt, Yann Bouteiller, Giovanni Beltrame +2
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, a…
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Jonathan Pilault, Amine Elhattami, Christopher Pal
Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: ove…
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal +1
Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable. In this paper, we…
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde, Julien Roy, Wonseok Jeon +3
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce t…