41 citations · 203 across the 38 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networks
Seongjin Park, Haedong Jeong, Tair Djanibekov +3
In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase. Along with the development of DNNs…
Variational Neural Temporal Point Process
Deokjun Eom, Sehyun Lee, Jaesik Choi
A temporal point process is a stochastic process that predicts which type of events is likely to happen and when the event will occur given a history of a sequence of events. There…