25 citations · 60 across the 7 of their papers we have counts for
7 papers
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
Bo Chen, Xingyi Cheng, Pan Li +12
Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or…
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Jing Gong, Minsheng Hao, Xingyi Cheng +6
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-c…
Predicting Software Performance with Divide-and-Learn
Jingzhi Gong, Tao Chen
Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on mac…
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference
Jing Gong, Hassaan Saadat, Hasindu Gamaarachchi +3
Edge training of Deep Neural Networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware…
Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes
Jingzhi Gong, Tao Chen
Learning and predicting the performance of a configurable software system helps to provide better quality assurance. One important engineering decision therein is how to encode the…
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
Yufan Jiang, Shuangzhi Wu, Jing Gong +5
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. Due to task specific of MMRC, it is…
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Shaohuai Shi, Xianhao Zhou, Shutao Song +21
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters. However, on public cloud clusters, due to the m…