목록Papers/Compression (38)
둔비의 공부공간
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks
(AAAI 2022) https://github.com/tjwhitaker/prune-and-tune-ensembles GitHub - tjwhitaker/prune-and-tune-ensembles Contribute to tjwhitaker/prune-and-tune-ensembles development by creating an account on GitHub. github.com Abstract Deeplearning의 ensemble은 효과적이고 좋지만, computational cost가 비싸다는 단점이 있다. 그래서, 이 논문에서는 빠르고 작은 cost로 scratch부터 다양한 모델을 학습할 필요가 없는 ensemble방법을 소개한다. 일단 single parent network를 하나 ..
Learning both Weights and Connections for Efficient Neural Networks
https://arxiv.org/abs/1506.02626 (Standford, Nvidia NIPS 2015) Learning both Weights and Connections for Efficient Neural Networks Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To arxiv.org ..