Model Compression in Practice: Lessons Learned from Practitioners
Creating On-device Machine Learning Experiences

Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

6 October 2023

Dominik Moritz

Papers citing "Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences"

10 / 10 papers shown

Title
PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing Cheng Deng Luoyang Sun Jiwen Jiang Yongcheng Zeng Xinjian Wu ... Haoyang Li Lei Chen Lionel M. Ni H. Zhang Jun Wang 66 0 0 15 Mar 2025
Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Tom Wallace Naser Ezzati-Jivan Beatrice Ombuki-Berman MQ 28 1 0 16 Jan 2025
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 39 3 0 06 Aug 2024
LLM in a flash: Efficient Large Language Model Inference with Limited Memory Keivan Alizadeh-Vahid Iman Mirzadeh Dmitry Belenko Karen Khatamifard Minsik Cho C. C. D. Mundo Mohammad Rastegari Mehrdad Farajtabar 70 104 0 12 Dec 2023
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation Rahul Mishra Hari Prabhat Gupta 25 7 0 30 Sep 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 128 679 0 31 Jan 2021
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 151 94 0 09 Feb 2020
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI Dakuo Wang Justin D. Weisz Michael J. Muller Parikshit Ram Werner Geyer Casey Dugan Y. Tausczik Horst Samulowitz Alexander G. Gray 156 312 0 05 Sep 2019
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 730 0 13 Dec 2018
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,214 0 17 Apr 2017