An Efficient Quantized Message Passing Receiver Design for SCMA Systems by Hao Cheng, Min Zhang, Ruoyu Su

“…In this sense, we propose a new quasi-uniform quantization scheme that can efficiently handle the dynamic range in the exchange of messages. …”
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Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge by Mohammed Alnemari, Nader Bagherzadeh

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Accelerating Energy-Efficient Federated Learning in Cell-Free Networks With Adaptive Quantization by Afsaneh Mahmoudi, Ming Xiao, Emil Bjornson

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Addressing Activation Outliers in LLMs: A Systematic Review of Post-Training Quantization Techniques by Patrik Czako, Gabor Kertesz, Sandor Szenasi

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Data-oriented optimized nonuniform quantization for CR-enhanced communication efficiency in federated learning by Shuai Luo, Qiming Wan, Hongrui Wang, Tianchun Xiang, Yang Wang, Xin He, Wei Zhang

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BiPruneFL: Computation and Communication Efficient Federated Learning With Binary Quantization and Pruning by Sangmin Lee, Hyeryung Jang

“…Existing solutions typically apply compression techniques such as quantization or pruning but only to a limited extent, constrained by the trade-off between model accuracy and compression efficiency. …”
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Learning from low precision samples by Ji In Choi, Madeleine Georges, Jung Ah Shin, Olivia Wang, Tiffany Zhu, Tapan Shah

Subjects: “…efficient machine learning…”
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Toward Generating Quality Test Questions and Answers Using Quantized Low-Rank Adapters in LLMs by Jebum Choi, Seongjun Hong, Seoyoon Hong, Jiyeon Park, Eun-Sung Jung

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Enhancing molecular property prediction with quantized GNN models by Areen Rasool, Jamshaid Ul Rahman, Rongin Uwitije

“…This paper presents a systematic approach to molecular networks by integrating GNN models with the DoReFa-Net quantization algorithm. The proposed method aims to enhance computational efficiency while maintaining predictive performance, enabling lightweight yet effective models suitable for molecular task. …”
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Rate distortion optimization for adaptive gradient quantization in federated learning by Guojun Chen, Kaixuan Xie, Wenqiang Luo, Yinfei Xu, Lun Xin, Tiecheng Song, Jing Hu

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Comparative assessment of efficiency quantitative methods of coproovoscopy by D. Yu. Derkachev, V. A. Orobets, I. V. Zaichenko

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Enacting efficient markets: The performativity of quantitative easing by Dylan Cassar

“…Quantitative easing (QE) has been a favourite tool of central banks in their post-financial crisis monetary policy apparatus. …”
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An energy efficient way for quantitative magnetization switching by Xin Li, Hanuman Singh, Jie Lin, Shuai Zhang, Bao Yi, Jyotirmoy Chatterjee, Zhuyun Xiao, Sucheta Mondal, Nobumichi Tamura, Rob N. Candler, Long You, Jeffrey Bokor, Jeongmin Hong

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Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy by Emre Ardic, Yakup Genc

“…To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. …”
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Efficient Spectral Compression of Wavelength-Shifting Soliton and Its Application in Integratable All-Optical Quantization by Chao Mei, Jinhui Yuan, Feng Li, Binbin Yan, Xinzhu Sang, Qiang Wu, Xian Zhou, Kuiru Wang, Chongxiu Yu, Gerald Farrell

“…In this paper, we numerically demonstrate efficient spectral compression (SPC) of wavelength-shifting soliton in a chalcogenide strip waveguide. …”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

Subjects: “…Communication-efficient federated learning…”
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Large language models for PHM: a review of optimization techniques and applications by Tingyi Yu, Junya Tang, Qingyun Yu, Li Li, Ying Liu, Raul Poler

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HLQ: Hardware-Friendly Logarithmic Quantization Aware Training for Power-Efficient Low-Precision CNN Models by Dahun Choi, Juntae Park, Hyun Kim

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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization by Joonhyuk Yoo, Guenwoo Ban

“…By analyzing previous outlier-handling techniques using structural similarity (SSIM) measurement results, we demonstrated that OAQ significantly reduced the negative impact of outliers while maintaining computational efficiency. Notably, OAQ was orthogonal to existing quantization schemes, making it compatible with various quantization methods without additional computational overhead. …”
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Deep learning model for precise and rapid prediction of tomato maturity based on image recognition by Muhammad Waseem, Muhammad Muzzammil Sajjad, Laraib Haider Naqvi, Yaqoob Majeed, Tanzeel Ur Rehman, Tayyaba Nadeem

Subjects: “…Efficient net…”
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