b'Bingzhe Wu'

research

∙ 09/20/2023

Is GPT4 a Good Trader?

Recently, large language models (LLMs), particularly GPT-4, have demonst...

0 Bingzhe Wu, et al. ∙

research

∙ 08/13/2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning

Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...

0 Jie Liao, et al. ∙

research

∙ 08/12/2023

Semantic Equivariant Mixup

Mixup is a well-established data augmentation technique, which can exten...

0 Zongbo Han, et al. ∙

research

∙ 06/05/2023

Uncertainty in Natural Language Processing: Sources, Quantification, and Applications

As a main field of artificial intelligence, natural language processing ...

0 Mengting Hu, et al. ∙

research

∙ 06/02/2023

Calibrating Multimodal Learning

Multimodal machine learning has achieved remarkable progress in a wide r...

0 Huan Ma. Qingyang Zhang, et al. ∙

research

∙ 05/29/2023

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Most named entity recognition (NER) systems focus on improving model per...

0 Zhen Zhang, et al. ∙

research

∙ 05/26/2023

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

The rapid development of digital economy has led to the emergence of var...

0 Qichao Wang, et al. ∙

research

∙ 04/09/2023

Reweighted Mixup for Subpopulation Shift

Subpopulation shift exists widely in many real-world applications, which...

0 Zongbo Han, et al. ∙

research

∙ 04/04/2023

SLPerf: a Unified Framework for Benchmarking Split Learning

Data privacy concerns has made centralized training of data, which is sc...

0 Tianchen Zhou, et al. ∙

research

∙ 04/03/2023

RPTQ: Reorder-based Post-training Quantization for Large Language Models

Large-scale language models (LLMs) have demonstrated outstanding perform...

0 Zhihang Yuan, et al. ∙

research

∙ 03/23/2023

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in...

3 Huan Ma, et al. ∙

research

∙ 03/23/2023

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

Post-training quantization (PTQ) is a popular method for compressing dee...

0 Zhihang Yuan, et al. ∙

research

∙ 02/23/2023

Federated Nearest Neighbor Machine Translation

To protect user privacy and meet legal regulations, federated learning (...

0 Yichao Du, et al. ∙

research

∙ 11/28/2022

Post-training Quantization on Diffusion Models

Denoising diffusion (score-based) generative models have recently achiev...

0 Yuzhang Shang, et al. ∙

research

∙ 11/16/2022

Learning with Noisy Labels over Imbalanced Subpopulations

Learning with Noisy Labels (LNL) has attracted significant attention fro...

0 Mingcai Chen, et al. ∙

research

∙ 10/20/2022

Vertical Federated Linear Contextual Bandits

In this paper, we investigate a novel problem of building contextual ban...

0 Zeyu Cao, et al. ∙

research

∙ 09/19/2022

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

Subpopulation shift wildly exists in many real-world machine learning ap...

0 Zongbo Han, et al. ∙

research

∙ 09/16/2022

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

The last decade has witnessed a prosperous development of computational ...

0 Lanqing Li, et al. ∙

research

∙ 05/20/2022

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Deep graph learning has achieved remarkable progresses in both business ...

0 Bingzhe Wu, et al. ∙

research

∙ 04/16/2022

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup

Recently, federated learning has emerged as a promising approach for tra...

0 Bingzhe Wu, et al. ∙

research

∙ 02/15/2022

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

Deep graph learning (DGL) has achieved remarkable progress in both busin...

0 Bingzhe Wu, et al. ∙

research

∙ 01/24/2022

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery – A Focus on Affinity Prediction Problems with Noise Annotations

AI-aided drug discovery (AIDD) is gaining increasing popularity due to i...

1 Yuanfeng Ji, et al. ∙

research

∙ 12/14/2021

Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis

Recently, generalization bounds of the non-convex empirical risk minimiz...

0 Bingzhe Wu, et al. ∙

research

∙ 05/04/2021

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

Tensor computations overwhelm traditional general-purpose computing devi...

0 Qingcheng Xiao, et al. ∙

research

∙ 12/17/2020

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

Deep Neural Networks (DNNs) have achieved remarkable progress in various...

0 Chaochao Chen, et al. ∙

research

∙ 11/06/2020

ASFGNN: Automated Separated-Federated Graph Neural Network

Graph Neural Networks (GNNs) have achieved remarkable performance by tak...

0 Longfei Zheng, et al. ∙

research

∙ 09/19/2020

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

Dynamic inference is a feasible way to reduce the computational cost of ...

0 Zhihang Yuan, et al. ∙

research

∙ 09/04/2020

A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

Transfer learning is widely used for transferring knowledge from a sourc...

0 Cen Chen, et al. ∙

research

∙ 05/25/2020

Privacy-Preserving Graph Neural Network for Node Classification

Recently, Graph Neural Network (GNN) has achieved remarkable progresses ...

0 Jun Zhou, et al. ∙

research

∙ 03/05/2020

Practical Privacy Preserving POI Recommendation

Point-of-Interest (POI) recommendation has been extensively studied and ...

0 Chaochao Chen, et al. ∙

research

∙ 02/06/2020

Secure Social Recommendation based on Secret Sharing

Nowadays, privacy preserving machine learning has been drawing much atte...

0 Chaochao Chen, et al. ∙

research

∙ 11/16/2019

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

Recently, dynamic inference has emerged as a promising way to reduce the...

0 Zhihang Yuan, et al. ∙

research

∙ 10/05/2019

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

Bayesian deep learning is recently regarded as an intrinsic way to chara...

0 Bingzhe Wu, et al. ∙

research

∙ 08/21/2019

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

In this paper, we aim to understand the generalization properties of gen...

0 Bingzhe Wu, et al. ∙

research

∙ 06/03/2019

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

Recently, deep learning as a service (DLaaS) has emerged as a promising ...

0 Peichen Xie, et al. ∙

research

∙ 05/30/2019

P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

Recently, deep convolutional neural networks (CNNs) have achieved great ...

0 Bingzhe Wu, et al. ∙

research

∙ 06/30/2018

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

Pathological glomerulus classification plays a key role in the diagnosis...

0 Bingzhe Wu, et al. ∙

research

∙ 12/16/2017

SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution

Single image super resolution (SISR) is to reconstruct a high resolution...

0 Bingzhe Wu, et al. ∙

Bingzhe Wu

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