Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis-Reference-Cited by-同舟云学术

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Published:2024-07-08 Issue: Volume:33 Page:1-5
ISSN:
Container-title:2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM)
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Author:

Li Hongkang¹,Wang Meng¹,Zhang Shuai²,Liu Sijia³,Chen Pin-Yu⁴

Affiliation:

1. Computer, and System Engineering Rensselaer Polytechnic Institute,Dept. Electrical,Troy,NY,USA

2. New Jersey Institute of Technology,Dept. Data Science,Newark,NJ,USA

3. Michigan State University,Dept. Computer Science and Engineering,East Lansing,MI,USA

4. IBM Thomas J. Watson Research Center,Yorktown Heights,NY,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10636308/10636358/10636559.pdf?arnumber=10636559

Reference40 articles.

1. Language mod-els are few-shot learners;Brown;Advances in neural information processing systems,2020

2. Sparks of artificial general intelligence: Early experiments with gpt-4;Bubeck;arXiv preprint,2023

3. An image is worth 16x16 words: Transformers for image recognition at scale;Dosovitskiy,2021

4. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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