Leveraging Adaptive Hyper Parameter Tuning for Automated Machine Learning-Reference-Cited by-同舟云学术

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Leveraging Adaptive Hyper Parameter Tuning for Automated Machine Learning

Published:2024-01-29 Issue: Volume: Page:1-7
ISSN:
Container-title:2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)
language:
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Author:

Nidhya M.S.¹,Saraswat Nidhi²,Kumar Prashant³

Affiliation:

1. Computer Science and Information Technology, Jain (Deemed to be University),Bangalore,India

2. Sanskriti University,Department of Computer Science Engineering,Mathura,Uttar Pradesh,India

3. College of Engineering, Teetthanker Mahaveer University,Moradabad,Uttar Pradesh,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10470457/10470444/10470671.pdf?arnumber=10470671

Reference15 articles.

1. Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

2. Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model

3. Automatic depression severity assessment with deep learning using parameter-efficient tuning

4. DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework;Wang;arXiv preprint,2023

5. AutoML-GPT: Automatic Machine Learning with GPT;Zhang;arXiv preprint,2023

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