no code implementations • ECCV 2020 • Zhihang Yuan, Bingzhe Wu, Guangyu Sun, Zheng Liang, Shiwan Zhao, Weichen Bi
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
no code implementations • 23 Mar 2023 • Zhihang Yuan, Jiawei Liu, Jiaxiang Wu, Dawei Yang, Qiang Wu, Guangyu Sun, Wenyu Liu, Xinggang Wang, Bingzhe Wu
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
1 code implementation • 23 Mar 2023 • Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, QinGhua Hu, Bingzhe Wu
Large language models have demonstrated surprising ability to perform in-context learning, i. e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.
no code implementations • 23 Feb 2023 • Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.
no code implementations • 28 Nov 2022 • Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, Yan Yan
These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise.
no code implementations • 16 Nov 2022 • Mingcai Chen, Yu Zhao, Bing He, Zongbo Han, Bingzhe Wu, Jianhua Yao
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
no code implementations • 20 Oct 2022 • Zeyu Cao, Zhipeng Liang, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin Zhao, Bingzhe Wu
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i. e., contextual information is vertically distributed over different departments.
1 code implementation • 19 Sep 2022 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
1 code implementation • 16 Sep 2022 • Lanqing Li, Liang Zeng, Ziqi Gao, Shen Yuan, Yatao Bian, Bingzhe Wu, Hengtong Zhang, Yang Yu, Chan Lu, Zhipeng Zhou, Hongteng Xu, Jia Li, Peilin Zhao, Pheng-Ann Heng
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
2 code implementations • 15 Jun 2022 • Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 16 Apr 2022 • Bingzhe Wu, Zhipeng Liang, Yuxuan Han, Yatao Bian, Peilin Zhao, Junzhou Huang
In this paper, we propose a general framework to solve the above two challenges simultaneously.
no code implementations • 15 Feb 2022 • Bingzhe Wu, Jintang Li, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including adversarial attacks, inherent noise, and distribution shift.
1 code implementation • 24 Jan 2022 • Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao Bian
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
1 code implementation • 4 May 2021 • Qingcheng Xiao, Size Zheng, Bingzhe Wu, Pengcheng Xu, Xuehai Qian, Yun Liang
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
no code implementations • 17 Dec 2020 • Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang, Xiaolin Zheng, Bingzhe Wu, Cen Chen, Li Wang, Jianwei Yin
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
no code implementations • 6 Nov 2020 • Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, Benyu Zhang
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
no code implementations • 13 Oct 2020 • Junming Ma, Chaofan Yu, Aihui Zhou, Bingzhe Wu, Xibin Wu, Xingyu Chen, Xiangqun Chen, Lei Wang, Donggang Cao
We present S3ML, a secure serving system for machine learning inference in this paper.
no code implementations • 19 Sep 2020 • Zhihang Yuan, Xin Liu, Bingzhe Wu, Guangyu Sun
The inference of a input sample can exit from early stage if the prediction of the stage is confident enough.
no code implementations • 4 Sep 2020 • Cen Chen, Bingzhe Wu, Minghui Qiu, Li Wang, Jun Zhou
To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.
no code implementations • 25 May 2020 • Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
no code implementations • 11 Mar 2020 • Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang, Jun Zhou, Shuang Yang
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
2 code implementations • 10 Mar 2020 • Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng, Bingzhe Wu
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
no code implementations • 5 Mar 2020 • Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjin Fang, Li Wang, Yuan Qi, Xiaolin Zheng
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
no code implementations • 6 Feb 2020 • Chaochao Chen, Liang Li, Bingzhe Wu, Cheng Hong, Li Wang, Jun Zhou
It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.
no code implementations • 16 Nov 2019 • Zhihang Yuan, Bingzhe Wu, Zheng Liang, Shiwan Zhao, Weichen Bi, Guangyu Sun
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
no code implementations • 5 Oct 2019 • Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan YAO, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
no code implementations • NeurIPS 2019 • Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
no code implementations • 3 Jun 2019 • Peichen Xie, Bingzhe Wu, Guangyu Sun
Specifically, we use homomorphic encryption to protect a client's raw data and use Bayesian neural networks to protect the DNN weights in a cloud server.
no code implementations • CVPR 2019 • Bingzhe Wu, Shiwan Zhao, Guangyu Sun, Xiaolu Zhang, Zhong Su, Caihong Zeng, Zhihong Liu
(2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset.
no code implementations • 30 Jun 2018 • Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun
Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).
no code implementations • 16 Dec 2017 • Bingzhe Wu, Haodong Duan, Zhichao Liu, Guangyu Sun
In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks.