Ruihao Gong | DeepAI

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∙ 08/08/2023

Lossy and Lossless (L^2) Post-training Model Size Compression

Deep neural networks have delivered remarkable performance and have been...

0 Yumeng Shi, et al. ∙

∙ 07/01/2023

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency

Extensive studies have shown that deep learning models are vulnerable to...

0 Yan Wang, et al. ∙

∙ 04/18/2023

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges...

0 Xiuying Wei, et al. ∙

∙ 09/28/2022

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effecti...

0 Shiyu Tang, et al. ∙

∙ 09/27/2022

Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models

Transformer architecture has become the fundamental element of the wides...

0 Xiuying Wei, et al. ∙

∙ 03/11/2022

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Recently, post-training quantization (PTQ) has driven much attention to ...

0 Xiuying Wei, et al. ∙

∙ 11/05/2021

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Model quantization has emerged as an indispensable technique to accelera...

0 Yuhang Li, et al. ∙

∙ 09/25/2021

Distribution-sensitive Information Retention for Accurate Binary Neural Network

Model binarization is an effective method of compressing neural networks...

0 Haotong Qin, et al. ∙

∙ 09/11/2021

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...

4 Shiyu Tang, et al. ∙

∙ 09/02/2021

Real World Robustness from Systematic Noise

Systematic error, which is not determined by chance, often refers to the...

0 Yan Wang, et al. ∙

∙ 06/13/2021

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

Spiking Neural Network (SNN) has been recognized as one of the next gene...

0 Yuhang Li, et al. ∙

∙ 03/01/2021

Diversifying Sample Generation for Accurate Data-Free Quantization

Quantization has emerged as one of the most prevalent approaches to comp...

0 Xiangguo Zhang, et al. ∙

∙ 02/10/2021

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

We study the challenging task of neural network quantization without end...

12 Yuhang Li, et al. ∙

∙ 11/19/2020

Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization

User data confidentiality protection is becoming a rising challenge in t...

0 Yuhang Li, et al. ∙

∙ 03/31/2020

Binary Neural Networks: A Survey

The binary neural network, largely saving the storage and computation, s...

22 Haotong Qin, et al. ∙

∙ 03/17/2020

Efficient Bitwidth Search for Practical Mixed Precision Neural Network

Network quantization has rapidly become one of the most widely used meth...

14 Yuhang Li, et al. ∙

∙ 12/29/2019

Towards Unified INT8 Training for Convolutional Neural Network

Recently low-bit (e.g., 8-bit) network quantization has been extensively...

21 Feng Zhu, et al. ∙

∙ 09/26/2019

Balanced Binary Neural Networks with Gated Residual

Binary neural networks have attracted numerous attention in recent years...

5 Mingzhu Shen, et al. ∙

∙ 09/24/2019

IR-Net: Forward and Backward Information Retention for Highly Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neur...

1 Haotong Qin, et al. ∙

∙ 08/14/2019

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Hardware-friendly network quantization (e.g., binary/uniform quantizatio...

4 Ruihao Gong, et al. ∙

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