10.1k citations · 21.9k across the 280 of their papers we have counts for
29 papers · 1 filter
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
Yang Shu, Zhangjie Cao, Jinghan Gao +3
Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find tha…
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Xuming Hu, Chenwei Zhang, Yawen Yang +4
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. Existing works either utilize self-training sche…
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
Haoran Yang, Hongxu Chen, Lin Li +2
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via le…
A Survey on Deep Learning Event Extraction: Approaches and Applications
Qian Li, Jianxin Li, Jiawei Sheng +8
Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on dee…
Dual Adversarial Variational Embedding for Robust Recommendation
Qiaomin Yi, Ning Yang, Philip S. Yu
Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and…
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Mehrnaz Najafi, Lifang He, Philip S. Yu
With recent advances in data collection from multiple sources, multi-view data has received significant attention. In multi-view data, each view represents a different perspective…