206 citations · 707 across the 16 of their papers we have counts for
4 papers · 1 filter
FILIP: Fine-grained Interactive Language-Image Pre-Training
Lewei Yao, Runhui Huang, Lu Hou +7
Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via…
Towards Efficient Post-training Quantization of Pre-trained Language Models
Haoli Bai, Lu Hou, Lifeng Shang +3
Network quantization has gained increasing attention with the rapid growth of large pre-trained language models~(PLMs). However, most existing quantization methods for PLMs follow…
Improved OOD Generalization via Adversarial Training and Pre-training
Mingyang Yi, Lu Hou, Jiacheng Sun +4
Recently, learning a model that generalizes well on out-of-distribution (OOD) data has attracted great attention in the machine learning community. In this paper, after defining OO…
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…