1 code implementation • ECCV 2020 • Jaekyeom Kim, Hyoungseok Kim, Gunhee Kim
Few-shot learning is an important research problem that tackles one of the greatest challenges of machine learning: learning a new task from a limited amount of labeled data.
no code implementations • 26 Apr 2024 • Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors.
no code implementations • 13 Mar 2024 • Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee
The primary limitation of large language models (LLMs) is their restricted understanding of the world.
no code implementations • ICLR 2022 • Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim
To address this issue, we propose Lipschitz-constrained Skill Discovery (LSD), which encourages the agent to discover more diverse, dynamic, and far-reaching skills.
1 code implementation • NeurIPS 2021 • Seohong Park, Jaekyeom Kim, Gunhee Kim
SAR can handle the stochasticity of environments by adaptively reacting to changes in states during action repetition.
1 code implementation • 27 Jun 2021 • Jaekyeom Kim, Seohong Park, Gunhee Kim
Having the ability to acquire inherent skills from environments without any external rewards or supervision like humans is an important problem.
1 code implementation • ICLR 2021 • Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim
We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable.
1 code implementation • 2 Oct 2018 • Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song
Reinforcement learning algorithms struggle when the reward signal is very sparse.
no code implementations • 27 Sep 2018 • HyoungSeok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song
Policy optimization struggles when the reward feedback signal is very sparse and essentially becomes a random search algorithm until the agent stumbles upon a rewarding or the goal state.