454 citations · 2.5k across the 132 of their papers we have counts for
12 papers · 1 filter
Coarse-to-Fine Contrastive Learning on Graphs
Peiyao Zhao, Yuangang Pan, Xin Li +3
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised ma…
One-shot Machine Teaching: Cost Very Few Examples to Converge Faster
Chen Zhang, Xiaofeng Cao, Yi Chang +1
Artificial intelligence is to teach machines to take actions like humans. To achieve intelligent teaching, the machine learning community becomes to think about a promising topic n…
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
Bowen Xing, Ivor W. Tsang
Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the d…
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling
Bowen Xing, Ivor W. Tsang
Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions. However, they treat all labels and label embeddi…
Data-Efficient Learning via Minimizing Hyperspherical Energy
Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning.…
LADDER: Latent Boundary-guided Adversarial Training
Xiaowei Zhou, Ivor W. Tsang, Jie Yin
Deep Neural Networks (DNNs) have recently achieved great success in many classification tasks. Unfortunately, they are vulnerable to adversarial attacks that generate adversarial e…
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao, Weixin Bu, Shengjun Huang +4
Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the g…
Black-box Generalization of Machine Teaching
Xiaofeng Cao, Yaming Guo, Ivor W. Tsang +1
Hypothesis-pruning maximizes the hypothesis updates for active learning to find those desired unlabeled data. An inherent assumption is that this learning manner can derive those u…
Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning
Bowen Xing, Ivor W. Tsang
Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task. However, we discover that existing syntax-based models su…
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Yan Zhang, Changyu Li, Ivor W. Tsang +5
Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve th…
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition
Bowen Xing, Ivor W. Tsang
The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. I…
DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification
Bowen Xing, Ivor Tsang
In aspect-level sentiment classification (ASC), state-of-the-art models encode either syntax graph or relation graph to capture the local syntactic information or global relational…