the archive
#distributed training
2 resultscs.LG2019
An Experimental Evaluation of Large Scale GBDT Systems
Fangcheng Fu, Jiawei Jiang, Yingxia Shao +1
The paper evaluates how different data management strategies affect the performance of distributed gradient boosting decision tree (GBDT) training and introduces a new system, Vero…
#gradient boosting#distributed training#data partitioning#storage formats
cs.LG2019
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
The paper presents Chainer, a Python-based deep learning framework that offers a NumPy-like API, dynamic Define-by-Run computation graphs, GPU acceleration via CuPy, and support fo…
#deep learning framework#python#gpu acceleration#dynamic computation graph