227 citations · 357 across the 6 of their papers we have counts for
8 papers
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari +2
Standard approaches to sequential decision-making exploit an agent's ability to continually interact with its environment and improve its control policy. However, due to safety, et…
Vision-Language Models as Success Detectors
Yuqing Du, Ksenia Konyushkova, Misha Denil +5
Detecting successful behaviour is crucial for training intelligent agents. As such, generalisable reward models are a prerequisite for agents that can learn to generalise their beh…
Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…
Neural Programmer-Interpreters
Scott Reed, Nando de Freitas
We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components:…
ACDC: A Structured Efficient Linear Layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard +1
The linear layer is one of the most pervasive modules in deep learning representations. However, it requires parameters and operations. These costs can be prohibi…
Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization pract…