Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training-Reference-Cited by-同舟云学术

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Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training

Published:2023-12-15 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Big Data (BigData)
language:
Short-container-title:

Author:

Qin Lianke¹,Mitra Saayan²,Song Zhao²,Yang Yuanyuan³,Zhou Tianyi⁴

Affiliation:

1. University of California Santa Barbara,Department of Computer Science,Santa Barbara,CA

2. Adobe Research Adobe,San Jose,CA

3. University of Washington,Department of Computer Science,Seattle,WA

4. University of Southern California,Department of Computer Science,Los Angeles,CA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386943.pdf?arnumber=10386943

Reference81 articles.

1. THE LIGHT BULB PROBLEM

2. Speeding up sparsification with inner product search data structures;Song;arXiv preprint arXiv:2204.03209,2022

3. Fast algorithm for solving structured convex programs;Ye,2020

4. A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path

5. Breaking the linear iteration cost barrier for some well-known conditional gradient methods using maxip data-structures;Xu;Advances in Neural Information Processing Systems,2021

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1. Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training;2023 IEEE International Conference on Big Data (BigData);2023-12-15

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