92 citations · 209 across the 12 of their papers we have counts for
13 papers
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Shiqi Lin, Zhizheng Zhang, Xin Li +2
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks. However, in most cases, data augmentation is randomly performed for each trainin…
Simple Contrastive Representation Adversarial Learning for NLP Tasks
Deshui Miao, Jiaqi Zhang, Wenbo Xie +4
Self-supervised learning approach like contrastive learning is attached great attention in natural language processing. It uses pairs of training data augmentations to build a clas…
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Zihan Yan, Li Liu, Xin Li +4
Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximit…
Confounder Identification-free Causal Visual Feature Learning
Xin Li, Zhizheng Zhang, Guoqiang Wei +4
Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free o…
Vector-quantized Image Modeling with Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh +7
Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on bot…
LIQA: Lifelong Blind Image Quality Assessment
Jianzhao Liu, Wei Zhou, Jiahua Xu +3
Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deploy…
A Detector-oblivious Multi-arm Network for Keypoint Matching
Xuelun Shen, Qian Hu, Xin Li +1
This paper presents a matching network to establish point correspondence between images. We propose a Multi-Arm Network (MAN) to learn region overlap and depth, which can greatly i…
Local Patch AutoAugment with Multi-Agent Collaboration
Shiqi Lin, Tao Yu, Ruoyu Feng +3
Data augmentation (DA) plays a critical role in improving the generalization of deep learning models. Recent works on automatically searching for DA policies from data have achieve…
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
Kelly Kostopoulou, Hang Xu, Aritra Dutta +3
Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification proc…
Small and Practical BERT Models for Sequence Labeling
Henry Tsai, Jason Riesa, Melvin Johnson +3
We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU. Start…
Domain-adversarial Network Alignment
Huiting Hong, Xin Li, Yuangang Pan +1
Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain represe…
On Improving Deep Reinforcement Learning for POMDPs
Pengfei Zhu, Xin Li, Pascal Poupart +1
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments,…
On Improving Deep Reinforcement Learning for POMDPs
Pengfei Zhu, Xin Li, Pascal Poupart +1
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments,…