Search Results for author: Yuri Burda

Found 8 papers, 7 papers with code

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

4 code implementations6 Jan 2022 Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, Vedant Misra

In this paper we propose to study generalization of neural networks on small algorithmically generated datasets.


Exploration by Random Network Distillation

19 code implementations ICLR 2019 Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov

In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.

Montezuma's Revenge reinforcement-learning +2

Large-Scale Study of Curiosity-Driven Learning

4 code implementations ICLR 2019 Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros

However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent.

Atari Games SNES Games

Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

1 code implementation ICLR 2018 Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel

Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence.


On the Quantitative Analysis of Decoder-Based Generative Models

2 code implementations14 Nov 2016 Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse

The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities.

Importance Weighted Autoencoders

21 code implementations1 Sep 2015 Yuri Burda, Roger Grosse, Ruslan Salakhutdinov

The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference.

Density Estimation

Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing

no code implementations30 Dec 2014 Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov

Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function.

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