41 citations · 203 across the 38 of their papers we have counts for
40 papers
Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo, Jiwoo Lee, Janghoon Ju +3
We introduce a novel loss function, Covariance Loss, which is conceptually equivalent to conditional neural processes and has a form of regularization so that is applicable to many…
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations
Seongun Kim, Jaesik Choi
Deep policy networks enable robots to learn behaviors to solve various real-world complex tasks in an end-to-end fashion. However, they lack transparency to provide the reasons of…
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Seongun Kim, Kyowoon Lee, Jaesik Choi
Mutual information-based reinforcement learning (RL) has been proposed as a promising framework for retrieving complex skills autonomously without a task-oriented reward function t…
The Disharmony between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation between Activations
Inyoung Paik, Jaesik Choi
Deep neural networks, which employ batch normalization and ReLU-like activation functions, suffer from instability in the early stages of training due to the high gradient induced…
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
Kyowoon Lee, Seongun Kim, Jaesik Choi
For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to decide the most suitable navigation policy. However, most existing deep reinforcement…
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification
Nesma Mahmoud, Hanna Antson, Jaesik Choi +2
Image classification with deep neural networks has reached state-of-art with high accuracy. This success is attributed to good internal representation features that bypasses the di…