4 citations · 9 across the 7 of their papers we have counts for
7 papers
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers
Mahdi Heidari, Mohammad Mahdi Rahimi, Jaekyun Moon
The quadratic attention score matrix remains a central obstacle to extending Transformers to longer input lengths. Existing efficient attention methods usually reduce t…
PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusion
Humaira Kousar, Hasnain Irshad Bhatti, Jaekyun Moon
Efficient data selection is crucial for enhancing the training efficiency of deep neural networks and minimizing annotation requirements. Traditional methods often face high comput…
Pruning-based Data Selection and Network Fusion for Efficient Deep Learning
Humaira Kousar, Hasnain Irshad Bhatti, Jaekyun Moon
Efficient data selection is essential for improving the training efficiency of deep neural networks and reducing the associated annotation costs. However, traditional methods tend…
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
Wonjeong Choi, Jungwuk Park, Dong-Jun Han +2
Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving th…
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, Younghyun Park +2
Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to sh…
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
Seokil Ham, Jungwuk Park, Dong-Jun Han +1
While multi-exit neural networks are regarded as a promising solution for making efficient inference via early exits, combating adversarial attacks remains a challenging problem. I…
StableFDG: Style and Attention Based Learning for Federated Domain Generalization
Jungwuk Park, Dong-Jun Han, Jinho Kim +3
Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same. The f…