219 citations · 233 across the 17 of their papers we have counts for
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cs.LG2019
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system
Shixian Wen, Laurent Itti
Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks. However, there a…
cs.LG2019★ 1 cited
Beneficial perturbation network for continual learning
Shixian Wen, Laurent Itti
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during l…
cs.LG2019
Learning Causal State Representations of Partially Observable Environments
Amy Zhang, Zachary C. Lipton, Luis Pineda +5
Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose an algorithm to approximate causal states, which…