Fetching the paper…
Reading the bibliography…
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear.
Elementary Information Theory
Douglas S. Jones · 1979
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl E. Rasmussen and Christopher K. I. Williams · 2006
Earlier work this paper cites.
Optimal observation times in experimental epidemic processes
Alex R. Cook, Gavin J. Gibson, and Christopher A. Gilligan · 2008
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Radford M. Neal · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Batchbald: Efficient and diverse batch acquisition for deep Bayesian active learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2012
Earlier work this paper cites.
Gaussian processes for big data
James Hensman, Nicolò Fusi, and Neil D. Lawrence · 2013
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. de G. Matthews, James Hensman, Richard Turner, and Zoubin Ghahramani · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Variational inference: A review for statisticians
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Later among the works it cites.
DeepMind Control Suite, 2018
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller · 2018
Later among the works it cites.
Solving Rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Matthias Plappert Alex Paino, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
Later among the works it cites.
Unsupervised curricula for visual meta-reinforcement learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Unsupervised meta-learning for reinforcement learning
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Meta reinforcement learning with latent variable Gaussian processes
Steindór Sæmundsson, Katja Hofmann, and Marc P. Deisenroth · 2018
Cited alongside, same era.
Meta-learning surrogate models for sequential decision making
Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, and Yee Whye Teh · 2019
Later among the works it cites.
Challenges of real-world reinforcement learning
Daniel J. Mankowitz, Gabriel Dulac-Arnold, and Todd Hester · 2019
Later among the works it cites.
Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, and Liam Paull · 2020
Closest in time.
Automatic curriculum learning for deep RL: A short survey
Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, and Pierre-Yves Oudeyer · 2020
Closest in time.