Search Results for author: Bingzhe Wu

Found 32 papers, 7 papers with code

S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

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.

Neural Architecture Search

Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance

no code implementations23 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.

Benchmarking Data Augmentation +1

Fairness-guided Few-shot Prompting for Large Language Models

1 code implementation23 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.


Federated Nearest Neighbor Machine Translation

no code implementations23 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.

Federated Learning Machine Translation +4

Post-training Quantization on Diffusion Models

no code implementations28 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.

Denoising Noise Estimation +1

Learning with Noisy Labels over Imbalanced Subpopulations

no code implementations16 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.

Learning with noisy labels

Vertical Federated Linear Contextual Bandits

no code implementations20 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.

Multi-Armed Bandits

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

1 code implementation19 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.

Generalization Bounds

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery

1 code implementation16 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).

Drug Discovery Fairness

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift

no code implementations15 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.

Adversarial Attack Graph Learning

HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation

1 code implementation4 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.


Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

no code implementations17 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.

Privacy Preserving

ASFGNN: Automated Separated-Federated Graph Neural Network

no code implementations6 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.

ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference

no code implementations19 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.

A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

no code implementations4 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.

Transfer Learning

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 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.

Classification General Classification +2

Industrial Scale Privacy Preserving Deep Neural Network

no code implementations11 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.

Fraud Detection Privacy Preserving

A Survey of Adversarial Learning on Graphs

2 code implementations10 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.

Graph Clustering Link Prediction +1

Practical Privacy Preserving POI Recommendation

no code implementations5 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.

Federated Learning Privacy Preserving

Secure Social Recommendation based on Secret Sharing

no code implementations6 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.

Privacy Preserving Recommendation Systems

S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

no code implementations16 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).

Neural Architecture Search

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

no code implementations5 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.

Generalization Bounds

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

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.

BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference

no code implementations3 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.

Privacy Preserving

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

no code implementations30 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).

Classification Decision Making +2

SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution

no code implementations16 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.

Image Super-Resolution SSIM

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