29 citations · 39 across the 6 of their papers we have counts for
8 papers
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
Siyu Wang, Yi Rong, Shiqing Fan +6
The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) pa…
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan, Yi Rong, Chen Meng +10
It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…
FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads
Guoping Long, Jun Yang, Wei Lin
Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL…
Characterizing Deep Learning Training Workloads on Alibaba-PAI
Mengdi Wang, Chen Meng, Guoping Long +4
Modern deep learning models have been exploited in various domains, including computer vision (CV), natural language processing (NLP), search and recommendation. In practical AI cl…
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Changying Du, Jia He, Changde Du +3
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices…
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
Changying Du, Fuzhen Zhuang, Jia He +2
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize n…