Universal Features for High-Dimensional Learning and Inference-Reference-Cited by-同舟云学术

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Universal Features for High-Dimensional Learning and Inference

Published:2024 Issue:1-2 Volume:21 Page:1-299
ISSN:1567-2190
Container-title:Foundations and Trends® in Communications and Information Theory
language:en
Short-container-title:FNT in Communications and Information Theory

Author:

Huang Shao-Lun,Makur Anuran,Wornell Gregory W.,Zheng Lizhong

Publisher

Now Publishers

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Doeblin Coefficients and Related Measures;IEEE Transactions on Information Theory;2024-07

2. SA-SVD: Mitigating Bias in Face Recognition by Fair Representation Learning;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

3. Timely Communications for Remote Inference;IEEE/ACM Transactions on Networking;2024

4. Robust Cross-Modal Remote Sensing Image Retrieval via Maximal Correlation Augmentation;IEEE Transactions on Geoscience and Remote Sensing;2024

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