11 citations · 38 across the 9 of their papers we have counts for
5 papers · 1 filter
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
Minghui Qiu, Peng Li, Chengyu Wang +8
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP)…
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…
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…
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
Qinggang Zhou, Yawen Zhang, Pengcheng Li +4
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient de…
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling
Jun Yang, Fei Wang
Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. H…