2.7k citations · 6.5k across the 140 of their papers we have counts for
8 papers · 1 filter
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Mingyang Zhang, Hao Chen, Chunhua Shen +4
Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to ch…
A Survey on Efficient Training of Transformers
Bohan Zhuang, Jing Liu, Zizheng Pan +3
Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transforme…
DENSE: Data-Free One-Shot Federated Learning
Jie Zhang, Chen Chen, Bo Li +5
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low com…
FATNN: Fast and Accurate Ternary Neural Networks
Peng Chen, Bohan Zhuang, Chunhua Shen
Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision c…
Improving Generative Adversarial Networks with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu +3
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such pri…
Deep Learning for Anomaly Detection: A Review
Guansong Pang, Chunhua Shen, Longbing Cao +1
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still s…
Multi-marginal Wasserstein GAN
Jiezhang Cao, Langyuan Mo, Yifan Zhang +3
Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as…
Deep Weakly-supervised Anomaly Detection
Guansong Pang, Chunhua Shen, Huidong Jin +1
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved per…