48 citations · 70 across the 5 of their papers we have counts for
6 papers
Magnitude Attention-based Dynamic Pruning
Jihye Back, Namhyuk Ahn, Jangho Kim
Existing pruning methods utilize the importance of each weight based on specified criteria only when searching for a sparse structure but do not utilize it during training. In this…
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…
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation
Jangho Kim, Simyung Chang, Nojun Kwak
As edge devices become prevalent, deploying Deep Neural Networks (DNN) on edge devices has become a critical issue. However, DNN requires a high computational resource which is rar…
Prototype-based Personalized Pruning
Jangho Kim, Simyung Chang, Sungrack Yun +1
Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitabl…
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.…