431 citations · 844 across the 46 of their papers we have counts for
4 papers · 2 filters
Meta Adaptation using Importance Weighted Demonstrations
Kiran Lekkala, Sami Abu-El-Haija, Laurent Itti
Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dyn…
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…
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…
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…