24 citations · 51 across the 5 of their papers we have counts for
5 papers
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Data-free mixed-precision quantization using novel sensitivity metric
Donghyun Lee, Minkyoung Cho, Seungwon Lee +2
Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices. However, an inacces…
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector
Junghoon Seo, Seungwon Lee, Beomsu Kim +1
Automatic post-disaster damage detection using aerial imagery is crucial for quick assessment of damage caused by disaster and development of a recovery plan. The main problem prev…
Quantization for Rapid Deployment of Deep Neural Networks
Jun Haeng Lee, Sangwon Ha, Saerom Choi +2
This paper aims at rapid deployment of the state-of-the-art deep neural networks (DNNs) to energy efficient accelerators without time-consuming fine tuning or the availability of t…
Training Deep Neural Network in Limited Precision
Hyunsun Park, Jun Haeng Lee, Youngmin Oh +2
Energy and resource efficient training of DNNs will greatly extend the applications of deep learning. However, there are three major obstacles which mandate accurate calculation in…