284 citations · 921 across the 16 of their papers we have counts for
16 papers
Meta-Learning with a Geometry-Adaptive Preconditioner
Suhyun Kang, Duhun Hwang, Moonjung Eo +2
Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared in…
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
Wonguk Cho, Jinha Park, Taesup Kim
Continual domain shift poses a significant challenge in real-world applications, particularly in situations where labeled data is not available for new domains. The challenge of ac…
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
Minchang Kim, Yongjin Yang, Jung Hyun Ryu +1
Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically inv…
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia, Jonas Mueller, Taesup Kim +2
Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks while addressing the limitations of standard deep learning approaches, such…
Faster Deep Reinforcement Learning with Slower Online Network
Kavosh Asadi, Rasool Fakoor, Omer Gottesman +3
Deep reinforcement learning algorithms often use two networks for value function optimization: an online network, and a target network that tracks the online network with some dela…
Visual Concept Reasoning Networks
Taesup Kim, Sungwoong Kim, Yoshua Bengio
A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connec…