58 citations · 92 across the 19 of their papers we have counts for
19 papers
Delayed Commitment for Representation Readiness in Stage-wise Audio-Visual Learning
Xinmeng Xu, Haoran Xie, S. Joe Qin +3
Stage-wise audio-visual encoders propagate fused intermediate states across layers, making the formation of later representations depend on the readiness of earlier fusion states.…
Imagine a City: CityGenAgent for Procedural 3D City Generation
Zishan Liu, Zecong Tang, RuoCheng Wu +6
The automated generation of interactive 3D cities is a critical challenge with broad applications in autonomous driving, virtual reality, and embodied intelligence. While recent ad…
Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
Simi Job, Xiaohui Tao, Taotao Cai +3
Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve comple…
Causal Neighbourhood Learning for Invariant Graph Representations
Simi Job, Xiaohui Tao, Taotao Cai +2
Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the und…
Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy
Thanveer Shaik, Xiaohui Tao, Haoran Xie
Quantum Machine Unlearning has emerged as a foundational challenge at the intersection of quantum information theory privacypreserving computation and trustworthy artificial intell…
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
Sadia Qureshi, Thanveer Shaik, Xiaohui Tao +4
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' bec…
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis
Lingling Xu, Haoran Xie, S. Joe Qin +2
Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on par…
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
Matthew Squires, Xiaohui Tao, Soman Elangovan +4
Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social sti…
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
Matthew Squires, Xiaohui Tao, Soman Elangovan +5
Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent.…
RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning
Ao Lv, Guige Ouyang, Yongzhong Huang +2
Temporal Knowledge Graph (TKG) reasoning involves predicting future events based on historical information. However, due to the unpredictability of future events, this task is high…
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
Simi Job, Xiaohui Tao, Taotao Cai +3
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approxima…
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
Lingling Xu, Haoran Xie, Si-Zhao Joe Qin +2
With the continuous growth in the number of parameters of transformer-based pretrained language models (PLMs), particularly the emergence of large language models (LLMs) with billi…
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
Xinhong Chen, Zongxi Li, Yaowei Wang +3
The study of causal relationships between emotions and causes in texts has recently received much attention. Most works focus on extracting causally related clauses from documents.…
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job, Xiaohui Tao, Taotao Cai +4
In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive und…
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However,…
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environmen…
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-l…
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted…