Guangyu Sun | DeepAI

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Xin Liu
216 publications
Ming Liu
179 publications
Li Wang
151 publications
Jun Zhou
122 publications
Yang Chen
117 publications
Zhe Liu
107 publications
Junzhou Huang
106 publications
Lingxi Xie
105 publications
Xinggang Wang
104 publications
Ran He
100 publications
Yuan Yao
99 publications

∙ 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. ∙

∙ 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. ∙

∙ 10/12/2022

Latency-aware Spatial-wise Dynamic Networks

Spatial-wise dynamic convolution has become a promising approach to impr...

0 Yizeng Han, et al. ∙

∙ 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. ∙

∙ 12/12/2021

Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning

Video anomaly detection aims to identify abnormal events that occurred i...

0 Guangyu Sun, et al. ∙

∙ 11/24/2021

PTQ4ViT: Post-Training Quantization Framework for Vision Transformers

Quantization is one of the most effective methods to compress neural net...

0 Zhihang Yuan, et al. ∙

∙ 11/01/2021

GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing

Recently, Graph Convolutional Networks (GCNs) have become state-of-the-a...

0 Zhe Zhou, et al. ∙

∙ 10/18/2021

Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention

In recent years, transformer models have revolutionized Natural Language...

0 Zhe Zhou, et al. ∙

∙ 10/15/2021

PTQ-SL: Exploring the Sub-layerwise Post-training Quantization

Network quantization is a powerful technique to compress convolutional n...

0 Zhihang Yuan, et al. ∙

∙ 08/26/2021

GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

Sampling is a critical operation in the training of Graph Neural Network...

0 Xin Liu, et al. ∙

∙ 08/24/2021

METRO: A Software-Hardware Co-Design of Interconnections for Spatial DNN Accelerators

Tiled spatial architectures have proved to be an effective solution to b...

0 Zhao Wang, et al. ∙

∙ 05/11/2021

Agatha: Smart Contract for DNN Computation

Smart contract is one of the core features of Ethereum and has inspired ...

0 Zihan Zheng, et al. ∙

∙ 11/12/2020

Customizing Trusted AI Accelerators for Efficient Privacy-Preserving Machine Learning

The use of trusted hardware has become a promising solution to enable pr...

0 Peichen Xie, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 07/26/2019

Detection of Malfunctioning Smart Electricity Meter

In this paper, a method for malfunctioning smart meter detection, based ...

0 Ming Liu, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 01/07/2018

EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond

Edge computing has evolved to be a promising avenue to enhance the syste...

0 Chao Yao, et al. ∙

∙ 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. ∙

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