20 citations · 22 across the 7 of their papers we have counts for
4 papers · 1 filter
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Jaehyeon Son, Soochan Lee, Gunhee Kim
Recent studies have shown that Transformers can perform in-context reinforcement learning (RL) by imitating existing RL algorithms, enabling sample-efficient adaptation to unseen t…
Learning to Continually Learn with the Bayesian Principle
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son +1
In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non…
When Meta-Learning Meets Online and Continual Learning: A Survey
Jaehyeon Son, Soochan Lee, Gunhee Kim
Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive dataset…
Recasting Continual Learning as Sequence Modeling
Soochan Lee, Jaehyeon Son, Gunhee Kim
In this work, we aim to establish a strong connection between two significant bodies of machine learning research: continual learning and sequence modeling. That is, we propose to…