Results 121 to 130 of about 96,348 (272)

FedPop: Federated Population-based Hyperparameter Tuning

Proceedings of the AAAI Conference on Artificial Intelligence
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to conventional ML pipelines, the client local optimization and server aggregation procedure in FL are sensitive to the hyperparameter (HP) selection.
Chen, Haokun, Krompass, Denis, Gu, Jindong, Tresp, Volker   +3 more
openaire   +2 more sources

Design and Optimization of Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces Based on Conditional Multi‐Task Deep Learning

Advanced Science, EarlyView.
A conditional multi‐task deep learning framework is developed for designing and optimizing Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces (FHPEMs). This framework achieves joint spectro‐polarimetric learning and unified forward–inverse design.
Chenjie Gong, Haodong Shi, Qi Wang, Yingchao Li, Hongyu Sun, Jiayu Wang, Guanlin Li, Jian Zhang, Qiang Fu, Huilin Jiang   +9 more
wiley   +1 more source

An Environmental Sustainable Approach to Machine Learning, Training and Development

Sakarya University Journal of Computer and Information Sciences
Artificial intelligence has the potential to drive sustainability by minimizing the impact of machine learning (ML) development on the environment. However, many ML techniques, particularly ensemble methods like the Random Forest classifier, require ...
K Jegadeeswari, Rathipriya R
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Taming hyperparameter tuning in continuous normalizing flows using the JKO scheme. [PDF]

Sci Rep, 2023
Vidal A, Wu Fung S, Tenorio L, Osher S, Nurbekyan L.   +4 more
europepmc   +1 more source

Machine‐Learning‐Guided Design of Incommensurate Antiferroelectrics via Field‐Driven Phase Engineering

Advanced Science, EarlyView.
The key to enhancing the energy storage performance of antiferroelectrics lies in regulating the phase transition and reverse phase transition. A phase‐field‐machine learning framework is employed to predict the energy storage performance of Pb‐based incommensurate antiferroelectrics with multi‐scale regulation strategy, thereby revealing the dynamic ...
Ke Xu, Xiaoming Shi, Zhaochen Xi, Shouzhe Dong, Changqing Guo, Rongzhen Gao, Letao Yang, Jing Wang, Di Zhou, Houbing Huang   +9 more
wiley   +1 more source

Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites. [PDF]

ACS Omega, 2023
Le NQK, Xu L.
europepmc   +1 more source

Prompt Engineering Accelerates the Data‐Driven Discovery of Photocatalysts via an LLM‐Based Model Ensemble Strategy

Advanced Science, EarlyView.
This work establishes a pipeline that transforms fragmented literature into a structured database for graphitic carbon nitride photocatalyst discovery. A prompt‐engineered, cross‐model large language model ensemble automates high‐fidelity extraction, enabling interpretable machine learning to identify dominant performance descriptors. These data‐driven
Dianyuan Li, Xichen Sun, Shaohua Sun, Runzhou Wang, Miaomiao Zhang, Meng Xiao, Yue Wang, Yuezhou Zhang   +7 more
wiley   +1 more source

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning. [PDF]

Biomimetics (Basel), 2023
Ang KM, Lim WH, Tiang SS, Sharma A, Eid MM, Tawfeek SM, Khafaga DS, Alharbi AH, Abdelhamid AA.   +8 more
europepmc   +1 more source

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

Advanced Science, EarlyView.
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen, Haoyu Wang, Guixin Xu, Yuqin Yuan, Xinmeng Cai, Zhengwu Wei, Dongheng Zhang, Qibin Sun, Tianwei Zhang, Cuntai Guan, Yan Chen   +10 more
wiley   +1 more source