1 code implementation • 18 May 2025 • Hanchen Wang, Yixuan Wu, Yinan Feng, Peng Jin, Shihang Feng, Yiming Mao, James Wiskin, Baris Turkbey, Peter A. Pinto, Bradford J. Wood, Songting Luo, Yinpeng Chen, Emad Boctor, Youzuo Lin
Through comprehensive baseline experiments, we demonstrate that state-of-the-art deep learning methods surpass traditional physics-based approaches in both inference efficiency and reconstruction accuracy.
1 code implementation • 3 May 2025 • Tianyu Liu, Simeng Han, Xiao Luo, Hanchen Wang, Pan Lu, Biqing Zhu, Yuge Wang, Keyi Li, Jiapeng Chen, Rihao Qu, Yufeng Liu, Xinyue Cui, Aviv Yaish, Yuhang Chen, Minsheng Hao, Chuhan Li, Kexing Li, Arman Cohan, Hua Xu, Mark Gerstein, James Zou, Hongyu Zhao
Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research, yet their reliability and specific contributions to biomedical applications remain insufficiently characterized.
no code implementations • 24 Mar 2025 • Wei Huang, Hanchen Wang, Dong Wen, Wenjie Zhang, Ying Zhang, Xuemin Lin
Specifically, we first generate multiple diverse node matching matrices in parallel through a diffusion-based graph matching model.
no code implementations • 13 Feb 2025 • Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
This work systematically assessed learned reconstruction methods incorporating an approximated physical model for USCT imaging.
1 code implementation • 4 Dec 2024 • Namkyeong Lee, Yunhak Oh, Heewoong Noh, Gyoung S. Na, Minkai Xu, Hanchen Wang, Tianfan Fu, Chanyoung Park
Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery.
1 code implementation • 17 Oct 2024 • Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev
Finally, we demonstrate the effectiveness of DRAKES in generating DNA and protein sequences that optimize enhancer activity and protein stability, respectively, important tasks for gene therapies and protein-based therapeutics.
no code implementations • 15 Oct 2024 • Guangxin Su, Yifan Zhu, Wenjie Zhang, Hanchen Wang, Ying Zhang
In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning.
no code implementations • 25 Jul 2024 • Jianke Yu, Hanchen Wang, Chen Chen, Xiaoyang Wang, Lu Qin, Wenjie Zhang, Ying Zhang, Xijuan Liu
However, current research overlooks the real-world scenario of incomplete graphs. To address this gap, we introduce the Robust Incomplete Deep Attack Framework (RIDA).
no code implementations • 18 Jul 2024 • Yi Sheng, Hanchen Wang, Yipei Liu, Junhuan Yang, Weiwen Jiang, Youzuo Lin, Lei Yang
Notably, over 82% of samples achieved an SSIM above 0. 8, with nearly 61% exceeding 0. 85, highlighting the significant potential of our approach in improving USCT image reconstruction by efficiently utilizing sparse data.
1 code implementation • 19 Mar 2024 • Lincan Li, Hanchen Wang, Wenjie Zhang, Adelle Coster
In this work, we introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective state space models for STG learning by treating STG Network as a system, and employing the Spatial-Temporal Selective State Space Module (ST-S3M) to precisely focus on the selected STG latent features.
1 code implementation • 29 Jan 2024 • Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zhuoxinran Li, Bolei Zhou, Jian Tang
Deep generative models (DGMs) have been widely developed for graph data.
no code implementations • 6 Jan 2024 • Junhuan Yang, Hanchen Wang, Yi Sheng, Youzuo Lin, Lei Yang
Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface.
no code implementations • 30 Aug 2023 • Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
no code implementations • 28 Jul 2023 • Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang, Yinpeng Chen, Benjamin Consolvo, Zicheng Liu, Youzuo Lin
This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem.
1 code implementation • IEEE Transactions on Knowledge and Data Engineering 2023 • PDF Han Chen, Hanchen Wang, Hongmei Chen, Ying Zhang, Wenjie Zhang, Xuemin Lin
The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science.
no code implementations • 21 Jun 2023 • Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Yanhua Liu, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
no code implementations • 25 Sep 2022 • Bian Li, Hanchen Wang, Xiu Yang, Youzuo Lin
Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice.
1 code implementation • NeurIPS 2023 • Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
1 code implementation • 1 Jun 2022 • Dingmin Wang, Shengchao Liu, Hanchen Wang, Bernardo Cuenca Grau, Linfeng Song, Jian Tang, Song Le, Qi Liu
Graph Neural Networks (GNNs) are effective tools for graph representation learning.
no code implementations • Neurocomputing 2022 • Hanchen Wang, Yining Wang, Jianfeng Li, Tao Luo
This degree difference between equivalent entities poses a great challenge for entity alignment.
no code implementations • 25 Jan 2022 • Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang, Xuemin Lin
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
2 code implementations • 4 Nov 2021 • Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
no code implementations • NeurIPS 2021 • Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller
In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.
1 code implementation • ICLR 2022 • Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation.
no code implementations • 11 Sep 2021 • Tariq Alkhalifah, Hanchen Wang, Oleg Ovcharenko
This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i. e., shot gather, seismic image, etc.)
no code implementations • 12 Feb 2021 • Mohammadreza Noormandipour, Hanchen Wang
In this work, we propose a parameterised quantum circuit learning approach to point set matching problem.
1 code implementation • ICCV 2021 • Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
no code implementations • 28 Sep 2020 • Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt Kusner
There has recently been a flurry of exciting advances in deep learning models on point clouds.
1 code implementation • 12 May 2020 • Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin
We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.
no code implementations • 19 Apr 2020 • Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
1 code implementation • 18 Nov 2019 • Yun-Hao Cao, Jianxin Wu, Hanchen Wang, Joan Lasenby
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions.
1 code implementation • 22 Oct 2019 • Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti, Krishna P. Gummadi, Adrian Weller
We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.