activity
20182020
most citedDAPPLE: A Pipelined Data Parallel Approach for Training Large Models

29 citations · 65 across the 9 of their papers we have counts for

collaborators

18 papers

cs.SD20208 cited

INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices

Yiwu Yao, Yuchao Li, Chengyu Wang +8

The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices. In this paper, we present a novel quantized Winograd op…

cs.CV2020

One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction

Mengli Cheng, Minghui Qiu, Xing Shi +2

Structured information extraction from document images usually consists of three steps: text detection, text recognition, and text field labeling. While text detection and text rec…

cs.DC20205 cited

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…

cs.LG2020

Graph Structural-topic Neural Network

Qingqing Long, Yilun Jin, Guojie Song +2

Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on…

cs.DC202029 cited

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…

cs.CV2020

SwapText: Image Based Texts Transfer in Scenes

Qiangpeng Yang, Hongsheng Jin, Jun Huang +1

Swapping text in scene images while preserving original fonts, colors, sizes and background textures is a challenging task due to the complex interplay between different factors. I…