419 citations · 920 across the 71 of their papers we have counts for
5 papers · 1 filter
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
Yatao Li, Wanling Gao, Lei Wang +3
AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchma…
FLBench: A Benchmark Suite for Federated Learning
Yuan Liang, Yange Guo, Yanxia Gong +3
Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data i…
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation…
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Chunjie Luo, Xiwen He, Jianfeng Zhan +3
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains. The application of deep learning on the mobile and embedded devic…
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The resul…