논문요약
1.논문요약, SLEAP: A deep learning system for multi-animal pose tracking
Pereira, Talmo D., et al. "SLEAP: A deep learning system for multi-animal tracking." Nature methods 19.4 (2022): 486-495.SLEAP(Social LEAP Estimates A
2.– Understanding LSTM – a tutorial into Long Short-Term Memory Recurrent Neural Networks
#LSTM #UnderstandingLSTM
3.BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter,Department of Computer Science, University of Freiburg,Freiburg, Germany. Correspondence to: Stefan Falkners
4.(논문리뷰)Edge AI: Leveraging the Full Potential of Deep Learning
엣지-호환 모델을 얻기 위해서는 딥 러닝 모델 압축기술이 필수적이다. 이 섹션에서는 AI서비스를 클라우드에서 엣지로 마이그레이션하는 다양한 딥러닝 모델 압축기술을 소개합니다.간결한 네트워크 디자인은 파라미터의 수와 계산량을 줄이는 중요한 역할을 한다. 그러므로, 현대의
5.Activating More Pixels in Image Super-Resolution Transformer
Xiangyu Chen, Xintao Wang, Jiantao Zhou, and Chao Dong University of Macau 2Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences 3AR
6.Understanding the role of individual units in a deep neural network
aComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; bAdobe Research, Adobe Inc.,San
7.[논문리뷰]B-cos Networks: Alignment is All We Need for Interpretability
B-cos Networks: Alignment is All We Need for Interpretability Author Abstract This paper proposed a weight-input alignment method that replaces the