454 citations · 2.5k across the 132 of their papers we have counts for
17 papers · 1 filter
Taming Overconfident Prediction on Unlabeled Data from Hindsight
Jing Li, Yuangang Pan, Ivor W. Tsang
Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed…
TRIP: Refining Image-to-Image Translation via Rival Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
Relative attribute (RA), referring to the preference over two images on the strength of a specific attribute, can enable fine-grained image-to-image translation due to its rich sem…
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Baijiong Lin, Feiyang Ye, Yu Zhang +1
Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance different tasks to achieve good performance is a key problem. To achieve the task balancin…
Deep Safe Multi-Task Learning
Zhixiong Yue, Feiyang Ye, Yu Zhang +2
In recent years, Multi-Task Learning (MTL) has attracted much attention due to its good performance in many applications. However, many existing MTL models cannot guarantee that th…
Edge but not Least: Cross-View Graph Pooling
Xiaowei Zhou, Jie Yin, Ivor W. Tsang
Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been develope…
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang +2
Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real world. Any instance of MTS is generated from a hybrid dynamical system and their specific…
Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network
Bowen Xing, Ivor W. Tsang
Aspect-level sentiment classification (ASC) aims to predict the fine-grained sentiment polarity towards a given aspect mentioned in a review. Despite recent advances in ASC, enabli…
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Maosen Li, Siheng Chen, Yanning Shen +3
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative predictio…
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset posse…
Imitation Learning: Progress, Taxonomies and Challenges
Boyuan Zheng, Sunny Verma, Jianlong Zhou +2
Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors. Its success has been demonstra…
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis
Bowen Xing, Ivor W. Tsang
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the…
Neural Optimization Kernel: Towards Robust Deep Learning
Yueming Lyu, Ivor Tsang
Deep neural networks (NN) have achieved great success in many applications. However, why do deep neural networks obtain good generalization at an over-parameterization regime is st…
Distribution Matching for Machine Teaching
Xiaofeng Cao, Ivor W. Tsang
Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the stude…
Bayesian Active Learning by Disagreements: A Geometric Perspective
Xiaofeng Cao, Ivor W. Tsang
We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its core-set construction interacting with model uncertainty estimation. T…
Contrastive Attraction and Contrastive Repulsion for Representation Learning
Huangjie Zheng, Xu Chen, Jiangchao Yao +7
Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative sampl…
Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation
Yaxin Shi, Xiaowei Zhou, Ping Liu +1
In this paper, we revisit the Image-to-Image (I2I) translation problem with transition consistency, namely the consistency defined on the conditional data mapping between each data…
Human-Understandable Decision Making for Visual Recognition
Xiaowei Zhou, Jie Yin, Ivor Tsang +1
The widespread use of deep neural networks has achieved substantial success in many tasks. However, there still exists a huge gap between the operating mechanism of deep learning m…