Ruihao Gong
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    • AtomNet: Designing Tiny Models from Operators Under Extreme MCU Constraints
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    • ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
    • Pushing the Limit of Post-Training Quantization
    • Robust long-tailed recognition with distribution-aware adversarial example generation
    • Tool Playgrounds: A Comprehensive and Analyzable Benchmark for LLM Tool Invocation
    • LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
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    • Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
    • Generating Transferable Adversarial Examples against Vision Transformers
    • NNLQP: A Multi-Platform Neural Network Latency Query and Prediction System with An Evolving Database
    • QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
    • MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
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    • BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
    • RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
    • Forward and Backward Information Retention for Accurate Binary Neural Networks
    • Rotation Consistent Margin Loss for Efficient Low-bit Face Recognition
    • Towards Unified INT8 Training for Convolutional Neural Network
    • Balanced Binary Neural Networks with Gated Residual
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    • Binary neural networks: A survey
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Pushing the Limit of Post-Training Quantization

Jan 1, 2025·
Ruihao Gong
Ruihao Gong
,
Xianglong Liu
,
Yuhang Li
,
Yunqiang Fan
,
Xiuying Wei
,
Jinyang Guo
· 0 min read
Cite DOI
Type
Journal article
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Last updated on Jan 1, 2025
Ruihao Gong
Authors
Ruihao Gong
← ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding Jan 1, 2025
Robust long-tailed recognition with distribution-aware adversarial example generation Jan 1, 2025 →

© 2025 Ruihao Gong. This work is licensed under CC BY NC ND 4.0