Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning

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.

Few-Shot Learning

On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model

no code implementations • NAACL 2022 • Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, WooMyoung Park, Jung-Woo Ha, Nako Sung

Many recent studies on large-scale language models have reported successful in-context zero- and few-shot learning ability.

Few-Shot Learning In-Context Learning +1

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

2 code implementations • EMNLP 2021 • Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, WooMyoung Park, Nako Sung

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.

Few-Shot Learning In-Context Learning +1

EMI: Exploration with Mutual Information

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.

Continuous Control Reinforcement Learning (RL)

EMI: Exploration with Mutual Information Maximizing State and Action Embeddings

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.

Continuous Control

Convergence Analysis of Optimization Algorithms

no code implementations • 6 Jul 2017 • HyoungSeok Kim, JiHoon Kang, WooMyoung Park, SukHyun Ko, YoonHo Cho, DaeSung Yu, YoungSook Song, JungWon Choi

The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm.