47 citations · 87 across the 16 of their papers we have counts for
5 papers · 1 filter
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
Yuezhou Sun, Wenlong Zhao, Lijun Zhang +3
This paper investigates deep neural network (DNN) compression from the perspective of compactly representing and storing trained parameters. We explore the previously overlooked op…
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
Sian Jin, Chengming Zhang, Xintong Jiang +5
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during…
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning
Lijun Zhang, Xiao Liu, Hui Guan
Multi-task learning (MTL) jointly learns a set of tasks by sharing parameters among tasks. It is a promising approach for reducing storage costs while improving task accuracy for m…
Rethinking Hard-Parameter Sharing in Multi-Domain Learning
Lijun Zhang, Qizheng Yang, Xiao Liu +1
Hard parameter sharing in multi-domain learning (MDL) allows domains to share some of the model parameters to reduce storage cost while improving prediction accuracy. One common sh…
Scalable Graph Neural Network Training: The Case for Sampling
Marco Serafini, Hui Guan
Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging…