Results 111 to 120 of about 42,332 (292)

SciBERT Optimisation for Named Entity Recognition on NCBI Disease Corpus with Hyperparameter Tuning

Journal of Applied Informatics and Computing
Named Entity Recognition (NER) in the biomedical domain faces complex challenges due to the variety of medical terms and their context of use. Transformer-based models, such as SciBERT, have proven to be effective in natural language processing (NLP ...
Abu Salam, Syaiful Rizal Sidiq
doaj   +1 more source

Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning

2025 IEEE International Conference on eScience (eScience)
Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algorithms are crucial for navigating the vast and complex search spaces in auto-tuning. As is well known in the context of machine learning
Willemsen, F-.J., Nieuwpoort, R.V. van, Werkhoven, B.J.C. van   +2 more
openaire   +3 more sources

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Advanced Science, EarlyView.
Short‐range order in 2D transition metal dichalcogenides is revealed as a new design paradigm. Driven by chemical affinity and atomic size, it governs properties across scales. Weak ordering tunes site‐resolved magnetism and d‐band centers, while strong ordering eliminates gap states to open band gaps.
Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun   +3 more
wiley   +1 more source

Federated Learning With Automated Dual-Level Hyperparameter Tuning

IEEE Open Journal of Signal Processing
Federated Learning (FL) is a decentralized machine learning (ML) approach where multiple clients collaboratively train a shared model over several update rounds without exchanging local data.
Rakib Ul Haque, Panagiotis Markopoulos
doaj   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Transferable Deep Reinforcement Learning With Edge‐Contour‐Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation

Advanced Science, EarlyView.
This study presents an anatomical landmark‐guided DRL framework for autonomous wireless capsule endoscopy navigation. Using a lightweight edge‐contour‐depth fusion module, it achieves over 97% coverage across diverse gastric anatomies. To ensure reliability, a two‐stage sim‐to‐real pipeline with an adaptive dynamic programming controller mitigates ...
Haoxuan Wu, Haitao Gao, Qingyang Liu, Sishen Yuan, Haiyang Fang, Mingwu Su, Baijia Liang, Yongzun Yang, Long Bai, Wenzhen Dong, Dihong Xie, Shijian Su, Jiewen Lai, Shing Shin Cheng, Zhen Li, Xiuli Zuo, Hongliang Ren   +16 more
wiley   +1 more source

Shadow‐Calibrated Stereo Vision for Colorimetric Sweat Analysis

Advanced Science, EarlyView.
By establishing a mathematical model that reconstructs 3D structures through geometric features of object shadows under controlled illumination, and combining it with Convolutional Neural Network‐based 2D image analysis for volumetric calibration, this work enables highly accurate 3D morphological reconstruction.
Ting Xiao, Yuwen Yan, Miaorong Lin, Jiahui Chen, Jianxin Meng, Xiang Cui, Peng Zhang, Fengyu Li   +7 more
wiley   +1 more source

The use of geometric uncertainty data in aero engine structural analysis and design

, 2013
A gas turbine disc has three critical regions for which lifing calculations are essential: the assembly holes or weld areas, the hub region, and the blade-disc attachment area.
Deshpande, Aditya S.
core  

Leveraging Cloud Resource for Hyperparameter Tuning in Deep Learning Models

, 2022
The act of tweaking the hyperparameters is very vital in the enhancements of deep learning models, although it is expensive in terms of computational complexity.
Krishnateja Shiva
core  

Ultra‐Wide‐Field Noninvasive Imaging Through Scattering Media Via Physics‐Guided Deep Learning

Advanced Science, EarlyView.
We propose a physics‐guided adaptive dual‐domain learning method for ultra‐wide‐field noninvasive imaging through scattering media, namely UNI‐Net. Our method not only reduces the requirement for real experimental data by an order of magnitude but also enables clear imaging of complex scenes with an ultra‐large field of view, which is 164 times the OME
Lintao Peng, Mingwei He, Jeff Zhu, Sujit K. Sahoo, Liheng Bian, Cuong Dang   +5 more
wiley   +1 more source