63 citations · 319 across the 19 of their papers we have counts for
19 papers
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Wenxuan Zhang, Yue Deng, Bing Liu +2
Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen consi…
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.…
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Jianda Chen, Sinno Jialin Pan
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this pro…
Fast Graph Generation via Spectral Diffusion
Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan
Generating graph-structured data is a challenging problem, which requires learning the underlying distribution of graphs. Various models such as graph VAE, graph GANs, and graph di…
Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization
Danni Peng, Sinno Jialin Pan
To address distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains…
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation
Quanyu Long, Tianze Luo, Wenya Wang +1
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach. One of the difficulties is how to learn task discrimination in the absence of…
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Wang Lu, Jindong Wang, Yiqiang Chen +3
It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towar…
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Danni Peng, Sinno Jialin Pan, Jie Zhang +1
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the…
AdaRNN: Adaptive Learning and Forecasting of Time Series
Yuntao Du, Jindong Wang, Wenjie Feng +4
Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Tianbo Li, Tianze Luo, Yiping Ke +1
Attributed event sequences are commonly encountered in practice. A recent research line focuses on incorporating neural networks with the statistical model -- marked point processe…
Reinforcement Learning with Efficient Active Feature Acquisition
Haiyan Yin, Yingzhen Li, Sinno Jialin Pan +2
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem. To be successful, an agent needs to efficiently gath…
Integrating Deep Learning with Logic Fusion for Information Extraction
Wenya Wang, Sinno Jialin Pan
Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed f…
Transfer Value Iteration Networks
Junyi Shen, Hankz Hankui Zhuo, Jin Xu +2
Value iteration networks (VINs) have been demonstrated to have a good generalization ability for reinforcement learning tasks across similar domains. However, based on our experime…
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
Haiyan Yin, Jianda Chen, Sinno Jialin Pan
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield…
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Xin Dong, Shangyu Chen, Sinno Jialin Pan
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along…
Multi-task memory networks for category-specific aspect and opinion terms co-extraction
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier
In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization. However, each task by itself only provides…
Distributed Multi-Task Relationship Learning
Sulin Liu, Sinno Jialin Pan, Qirong Ho
Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-ta…
Transfer Hashing with Privileged Information
Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan +3
Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However…
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier +1
In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous stud…