82 citations · 216 across the 20 of their papers we have counts for
20 papers
Task-oriented Memory-efficient Pruning-Adapter
Guorun Wang, Jun Yang, Yaoru Sun
The Outstanding performance and growing size of Large Language Models has led to increased attention in parameter efficient learning. The two predominant approaches are Adapters an…
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
Xiaodong Yi, Shiwei Zhang, Lansong Diao +6
This paper proposes DisCo, an automatic deep learning compilation module for data-parallel distributed training. Unlike most deep learning compilers that focus on training or infer…
Efficient Pipeline Planning for Expedited Distributed DNN Training
Ziyue Luo, Xiaodong Yi, Guoping Long +4
To train modern large DNN models, pipeline parallelism has recently emerged, which distributes the model across GPUs and enables different devices to process different microbatches…
Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals
Maoyu Mao, Jun Yang
Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel…
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Jiandong Mu, Mengdi Wang, Feiwen Zhu +3
Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for appli…
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Shaokun Zhang, Xiawu Zheng, Chenyi Yang +7
Despite superior performance on various natural language processing tasks, pre-trained models such as BERT are challenged by deploying on resource-constraint devices. Most existing…