211 citations · 293 across the 6 of their papers we have counts for
10 papers
Optimal channel selection with discrete QCQP
Yeonwoo Jeong, Deokjae Lee, Gaon An +2
Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy ap…
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim +1
Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approxi…
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong +1
While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustnes…
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks.…
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Yeonwoo Jeong, Hyun Oh Song
We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation…
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization
Yeonwoo Jeong, Yoonsung Kim, Hyun Oh Song
We develop hierarchically quantized efficient embedding representations for similarity-based search and show that this representation provides not only the state of the art perform…