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20172022
most citedPathNet: Evolution Channels Gradient Descent in Super Neural Networks

643 citations · 787 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.LG2022

Hindering Adversarial Attacks with Implicit Neural Representations

Andrei A. Rusu, Dan A. Calian, Sven Gowal +1

We introduce the Lossy Implicit Network Activation Coding (LINAC) defence, an input transformation which successfully hinders several common adversarial attacks on CIFAR- class…

cs.LG20221 cited

Probing Transfer in Deep Reinforcement Learning without Task Engineering

Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao +2

We evaluate the use of original game curricula supported by the Atari 2600 console as a heterogeneous transfer benchmark for deep reinforcement learning agents. Game designers crea…

cs.LG201986 cited

Continual Unsupervised Representation Learning

Dushyant Rao, Francesco Visin, Andrei A. Rusu +3

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially…

cs.LG2019

Meta-Learning with Warped Gradient Descent

Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu +3

Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works…

cs.LG2018

Meta-Learning with Latent Embedding Optimization

Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski +4

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practica…