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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...
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07/01/2023
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to...
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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...
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09/28/2022
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Adversarial training has been demonstrated to be one of the most effecti...
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09/27/2022
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Transformer architecture has become the fundamental element of the wides...
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03/11/2022
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Recently, post-training quantization (PTQ) has driven much attention to ...
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11/05/2021
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Model quantization has emerged as an indispensable technique to accelera...
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09/25/2021
Distribution-sensitive Information Retention for Accurate Binary Neural Network
Model binarization is an effective method of compressing neural networks...
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09/11/2021
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...
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09/02/2021
Real World Robustness from Systematic Noise
Systematic error, which is not determined by chance, often refers to the...
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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...
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03/01/2021
Diversifying Sample Generation for Accurate Data-Free Quantization
Quantization has emerged as one of the most prevalent approaches to comp...
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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...
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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...
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03/31/2020
Binary Neural Networks: A Survey
The binary neural network, largely saving the storage and computation, s...
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03/17/2020
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Network quantization has rapidly become one of the most widely used meth...
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12/29/2019
Towards Unified INT8 Training for Convolutional Neural Network
Recently low-bit (e.g., 8-bit) network quantization has been extensively...
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09/26/2019
Balanced Binary Neural Networks with Gated Residual
Binary neural networks have attracted numerous attention in recent years...
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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...
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08/14/2019