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I am currently a PhD Candidate at The Hong Kong University of Science and Technology, advised by Prof. Xiaomeng Li and co-advised by Prof. Hao Wang at Rutgers University. Prior of this, I completed my Bachelor degree (with Honours) at The Australian National University, where I had the privilege of being advised by Senior Prof. Amanda Barnard and supervised by Dr. Amanda Parker. My research journey includes experiences as a Research Assistant at MIT's Computational Connectomics Group lead by Prof. Nir Shavit, where I had the opportunity to collaborate with Prof. Hao Wang and Prof. Lu Mi. Furthermore, I had served as a Research Assistant under the supervision of Dr. Gang Guo at NUS. Email / CV / Google Scholar / LinkedIn |
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My research interest is to apply Machine Learning to interdisciplinary fields.
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"*" indicates equal contribution, "_" indicates equal advising. |
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Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li ICLR, 2024 Paper/Code Interpretability We introduce Energy-Based Concept Bottleneck Models (ECBM) as a unified framework for concept-based prediction, concept correction, and fine-grained interpretations based on conditional probabilities. |
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Chen Zhao, Kai Wang, Qianji Jiao, Xinyue Xu, Yuanbi Yi, Penghui Li, Julian Merder, Ding He Geophysical Research Letters, 2024 Paper AI4Science Interpretability Machine learning models were built to correlate the molecular composition and biological reactivity at the world's largest reservoir. Shorter incubations result in a broader range of molecules disappearing, with a greater contribution of stochasticity. Tuning the machine learning model contributes to yield additional interpretability beyond its well-recognized predictive power.
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Xinyue Xu*, Yuhan Hsi*, Haonan Wang, Xiaomeng Li ICONIP, 2023 Paper/Code AI4Science Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. |
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Chen Zhao*, Xinyue Xu*, Hongmei Chen, Fengwen Wang, Penghui Li, Chen He, Quan Shi, Yuanbi Yi, Xiaomeng Li, Siliang Li, Ding He Environmental Science & Technology (ES&T), 2023 The 6th Xiamen Symposium on Marine Environmental Sciences, 2023 Paper AI4Science Interpretability Photochemical reactions are essential components altering dissolved organic matter (DOM) chemistry. We first used machine learning approaches to compatibly integrate existing irradiation experiments and provide novel insights into the estuarine DOM transformation processes. |
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Zhongren Chen*, Xinyue Xu*, Shengyi Jiang, Hao Wang, Lu Mi KDD Deep Learning on Graphs, 2022 arXiv AI4Science Small subgraphs (graphlets) are important features to describe fundamental units of a large network. Unfortunately due to the inherent complexity of this task, most of the existing methods are computationally intensive and inefficient. In this work, we propose GNNS, a novel representational learning framework that utilizes graph neural networks to sample subgraphs efficiently for estimating their frequency distribution. |
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Xinyue Xu, Xiang Ding, Zhenyue Qin, Yang Liu ICONIP, 2021 Paper Interpretability AI4Science We use a variety of classification models to recognize the positive cases of SARS. We conduct evaluation with two types of SARS datasets, numerical and categorical types. For the sake of more clear interpretability, we also generate explanatory rules for the models. |
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The source code is from Jon Barron's public academic website. |