no code implementations • 3 Aug 2024 • Hong Guan, Yancheng Wang, Lulu Xie, Soham Nag, Rajeev Goel, Niranjan Erappa Narayana Swamy, Yingzhen Yang, Chaowei Xiao, Jonathan Prisby, Ross Maciejewski, Jia Zou
Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms.
1 code implementation • 21 Jul 2024 • Yancheng Wang, Yingzhen Yang
In this paper, we propose a novel and compact transformer block, Transformer with Learnable Token Merging (LTM), or LTM-Transformer.
no code implementations • 14 Feb 2024 • Rajeev Goel, Utkarsh Nath, Yancheng Wang, Alvin C. Silva, Teresa Wu, Yingzhen Yang
To address this challenge, we propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is universally applicable to the training of all neural networks.
no code implementations • 14 Feb 2024 • Yancheng Wang, Yingzhen Yang
To the best of our knowledge, our theoretical result is among the first to theoretically demonstrate the advantage of low-rank learning in graph contrastive learning supported by strong empirical performance.
no code implementations • 22 Jan 2024 • Hong Guan, Summer Gautier, Rajan Hari Ambrish, Yancheng Wang, Chaowei Xiao, Yingzhen Yang, Jia Zou
It is challenging to select the right privacy-preserving mechanism for federated query processing over multiple private data silos.
no code implementations • 28 Aug 2023 • Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Yingzhen Yang
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints.
no code implementations • 19 Jan 2023 • Utkarsh Nath, Yancheng Wang, Yingzhen Yang
In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation.
1 code implementation • 16 Aug 2022 • Yu Cao, Yancheng Wang, Yifei Xue, Huiqing Zhang, Yizhen Lao
Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars.
1 code implementation • 27 May 2022 • Yancheng Wang, Yingzhen Yang
In this work, we propose a novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node representations.
1 code implementation • 4 Mar 2022 • Yancheng Wang, Ning Xu, Yingzhen Yang
Non-local attention module has been proven to be crucial for image restoration.
1 code implementation • 17 Feb 2022 • Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu
In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods.
1 code implementation • 11 Jul 2020 • Fu Xiong, Yang Xiao, Zhiguo Cao, Yancheng Wang, Joey Tianyi Zhou, Jianxi Wu
Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.
1 code implementation • CVPR 2020 • Yancheng Wang, Yang Xiao, Fu Xiong, Wenxiang Jiang, Zhiguo Cao, Joey Tianyi Zhou, Junsong Yuan
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
1 code implementation • 29 Jun 2018 • Yang Xiao, Jun Chen, Yancheng Wang, Zhiguo Cao, Joey Tianyi Zhou, Xiang Bai
To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed.