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20172023
most citedPOMO: Policy Optimization with Multiple Optima for Reinforcement Learning

135 citations · 198 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023★ 3 cited

Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

Byoungjip Kim, Sungik Choi, Dasol Hwang +2

Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources.…

cs.LG2021★ 28 cited

SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning

Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon +3

This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consist…

cs.LG2020★ 135 cited

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim +3

In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a gr…

cs.LG2020

DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim +1

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed sta…

cs.LG2020★ 1 cited

VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning

Jongwon Choi, Kwang Moo Yi, Jihoon Kim +5

Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard…