no code implementations • 17 Jun 2025 • Ruihao Xi, Xuekuan Wang, Yongcheng Li, Shuhua Li, Zichen Wang, Yiwei Wang, Feng Wei, Cairong Zhao
Generating realistic and controllable human motions, particularly those involving rich multi-character interactions, remains a significant challenge due to data scarcity and the complexities of modeling inter-personal dynamics.
no code implementations • 16 Jun 2025 • Edward Li, Zichen Wang, Jiahe Huang, Jeong Joon Park
We present a unified framework for solving partial differential equations (PDEs) using video-inpainting diffusion transformer models.
no code implementations • 15 Apr 2025 • Ekaterina Redekop, Mara Pleasure, Vedrana Ivezic, Zichen Wang, Kimberly Flores, Anthony Sisk, William Speier, Corey Arnold
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images.
no code implementations • 21 Mar 2025 • Runze Ma, Zhongyue Zhang, Zichen Wang, Chenqing Hua, Zhuomin Zhou, Fenglei Cao, Jiahua Rao, Shuangjia Zheng
Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions.
no code implementations • 7 Mar 2025 • Ekaterina Redekop, Zichen Wang, Rushikesh Kulkarni, Mara Pleasure, Aaron Chin, Hamid Reza Hassanzadeh, Brian L. Hill, Melika Emami, William Speier, Corey W. Arnold
This study presents the first comprehensive analysis of zero-shot forecasting with GPT-based foundational models in EHRs, introducing a novel pipeline that formulates medical concept prediction as a generative modeling task.
1 code implementation • 28 Feb 2025 • Xinyu Yuan, Zichen Wang, Marcus Collins, Huzefa Rangwala
Recent years have witnessed a surge in the development of protein structural tokenization methods, which chunk protein 3D structures into discrete or continuous representations.
no code implementations • 14 Feb 2025 • Can Chen, Karla-Luise Herpoldt, Chenchao Zhao, Zichen Wang, Marcus Collins, Shang Shang, Ron Benson
Antibodies are widely used as therapeutics, but their development requires costly affinity maturation, involving iterative mutations to enhance binding affinity. This paper explores a sequence-only scenario for affinity maturation, using solely antibody and antigen sequences.
1 code implementation • 17 Dec 2024 • Zichen Wang, Hao Miao, Senzhang Wang, Renzhi Wang, Jianxin Wang, Jian Zhang
Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving.
no code implementations • 4 Dec 2024 • Zhiheng Zhang, Zichen Wang
Network interference has attracted significant attention in the field of causal inference, encapsulating various sociological behaviors where the treatment assigned to one individual within a network may affect the outcomes of others, such as their neighbors.
no code implementations • CVPR 2025 • Tianyi Wang, Zichen Wang, Cong Wang, Yuanchao Shu, Ruilong Deng, Peng Cheng, Jiming Chen
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management.
1 code implementation • 29 Oct 2024 • Yingheng Wang, Zichen Wang, Gil Sadeh, Luca Zancato, Alessandro Achille, George Karypis, Huzefa Rangwala
Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design.
no code implementations • 29 Oct 2024 • Zihan Pengmei, Zhengyuan Shen, Zichen Wang, Marcus Collins, Huzefa Rangwala
In this work, we explored the possibility of using pre-trained Geom-GNNs as transferable and highly effective geometric descriptors for improved generalization.
no code implementations • 19 Oct 2024 • Zichen Wang, Yaokun Ji, Jianing Tian, Shuangjia Zheng
Our method leverages a set of structural homologous motifs that align with query structural constraints to guide the generative model in inversely optimizing antibodies according to desired design criteria.
no code implementations • 12 Sep 2024 • Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang Zong, Kimberly Flores, William Speier, Corey W. Arnold
To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning.
no code implementations • 9 Sep 2024 • Ruiqi Wang, Zichen Wang, Peiqi Gao, Mingzhen Li, Jaehwan Jeong, Yihang Xu, Yejin Lee, Carolyn M. Baum, Lisa Tabor Connor, Chenyang Lu
With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical.
1 code implementation • 2 Sep 2024 • Yanfeng Zhou, Lingrui Li, Zichen Wang, Guole Liu, Ziwen Liu, Ge Yang
So far, however, XNet still faces the limitations, including performance degradation when images lack high-frequency (HF) information, underutilization of raw images and insufficient fusion.
1 code implementation • 25 Jun 2024 • Jiahe Huang, Guandao Yang, Zichen Wang, Jeong Joon Park
We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models.
1 code implementation • 10 Jun 2024 • Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis
GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.
no code implementations • 14 May 2024 • Zichen Wang, Xi Deng, Ziyi Zhang, Wenzel Jakob, Steve Marschner
We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines.
1 code implementation • 15 Jan 2024 • Zichen Wang, Bo Yang, Haonan Yue, Zhenghao Ma
However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance. New methods are required to capture the distinctive local context for more robust novel object detection.
1 code implementation • 12 Jan 2024 • Bowen Shi, Peisen Zhao, Zichen Wang, Yuhang Zhang, Yaoming Wang, Jin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian, Xiaopeng Zhang
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
Ranked #1 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+2
1 code implementation • CVPR 2025 • Aditya Chetan, Guandao Yang, Zichen Wang, Steve Marschner, Bharath Hariharan
Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts.
no code implementations • 17 Oct 2023 • Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server.
1 code implementation • 5 Oct 2023 • Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, Rishita Anubhai
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
no code implementations • 2 Oct 2023 • Omid Bazgir, Zichen Wang, Ji Won Park, Marc Hafner, James Lu
Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions.
1 code implementation • 27 Sep 2023 • Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
no code implementations • 18 Sep 2023 • Jinsheng Pan, Zichen Wang, Weihong Qi, Hanjia Lyu, Jiebo Luo
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters.
1 code implementation • 7 Jun 2023 • Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, Xing Xie
Furthermore, we present a comprehensive analysis to understand the mystery behind prompt robustness and its transferability.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
no code implementations • 30 May 2023 • Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song, Mengdi Wang, Huazheng Wang
We propose the first study of adversarial attacks on online learning to rank.
no code implementations • 28 Mar 2023 • Jinsheng Pan, Weihong Qi, Zichen Wang, Hanjia Lyu, Jiebo Luo
There is a broad consensus that news media outlets incorporate ideological biases in their news articles.
1 code implementation • 16 Jan 2023 • Hanjia Lyu, Jinsheng Pan, Zichen Wang, Jiebo Luo
We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2, 200 manually labeled and 1. 8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner.
no code implementations • 9 Nov 2022 • Gil Sadeh, Zichen Wang, Jasleen Grewal, Huzefa Rangwala, Layne Price
In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
1 code implementation • 30 Sep 2022 • Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C. Price, Luis F. Voloch, George Karypis
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
2 code implementations • 13 Sep 2022 • Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, Robert A. Barton, George Karypis
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
no code implementations • 17 Feb 2022 • Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
1 code implementation • Bioinformatics, Volume 36, Issue Supplement_1 2020 • Zichen Wang, Mu Zhou, Corey Arnold
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
no code implementations • 18 Oct 2019 • Wenyuan Li, Zichen Wang, Yuguang Yue, Jiayun Li, William Speier, Mingyuan Zhou, Corey W. Arnold
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
no code implementations • 16 May 2019 • Wenyuan Li, Zichen Wang, Jiayun Li, Jennifer Polson, William Speier, Corey Arnold
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.