Zobrazeno 1 - 10
of 12 022
pro vyhledávání: '"Hyper Parameter Optimization"'
Autor:
Saadati, Yasaman, Amini, M. Hadi
Federated Learning (FL) is a decentralized learning approach that protects sensitive information by utilizing local model parameters rather than sharing clients' raw datasets. While this privacy-preserving method is widely employed across various app
Externí odkaz:
http://arxiv.org/abs/2411.12244
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks. However, LLMs are generally inefficient when it comes to fine-tuning hype
Externí odkaz:
http://arxiv.org/abs/2410.16309
Autor:
Wang, Liangzhi, Zhang, Jie, Gao, Yuan, Zhang, Jiliang, Wei, Guiyi, Zhou, Haibo, Zhuge, Bin, Zhang, Zitian
In this paper, we propose a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction models. An attention-based deep neural network (ADNN) is adopted as the prediction model, i.e., base-learner, for eac
Externí odkaz:
http://arxiv.org/abs/2409.14535
This study introduces SLLMBO, an innovative framework that leverages Large Language Models (LLMs) for hyperparameter optimization (HPO), incorporating dynamic search space adaptability, enhanced parameter landscape exploitation, and a hybrid, novel L
Externí odkaz:
http://arxiv.org/abs/2410.20302
Autor:
Fowler, J., Jensen-Clem, Rebecca, van Kooten, Maaike A. M., Chambouleyron, Vincent, Cetre, Sylvain
The direct imaging and characterization of exoplanets requires extreme adaptive optics (XAO), achieving exquisite wavefront correction (upwards of 90$\%$ Strehl) over a narrow field of view (a few arcseconds). For these XAO systems the temporal error
Externí odkaz:
http://arxiv.org/abs/2407.11187
Incorporating scientific knowledge into deep learning (DL) models for materials-based simulations can constrain the network's predictions to be within the boundaries of the material system. Altering loss functions or adding physics-based regularizati
Externí odkaz:
http://arxiv.org/abs/2405.08580
The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; bot
Externí odkaz:
http://arxiv.org/abs/2403.12237
Autor:
Yedida, Rahul, Saha, Snehanshu
We propose a novel white-box approach to hyper-parameter optimization. Motivated by recent work establishing a relationship between flat minima and generalization, we first establish a relationship between the strong convexity of the loss and its fla
Externí odkaz:
http://arxiv.org/abs/2402.05025
Autor:
Kaur, Balraj Preet1 (AUTHOR), Singh, Harpreet2 (AUTHOR), Hans, Rahul1 (AUTHOR), Sharma, Sanjeev Kumar3 (AUTHOR), Sharma, Chetna4 (AUTHOR), Hassan, Md. Mehedi5 (AUTHOR) mehedihassan@ieee.org
Publikováno v:
PLoS ONE. 12/2/2024, Vol. 19 Issue 12, p1-32. 32p.
