Results 121 to 130 of about 46,859 (313)
Implementation of Bayesian Hyperparameter Optimization for Predicting Student Dropout in Sub-Saharan Africa Secondary Schools [PDF]
Yuda N. Mnyawami +2 more
openalex +1 more source
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 +3 more
wiley +1 more source
Military aircraft detection from aerial and satellite imagery is crucial for strategic surveillance and intelligence. This study evaluated the impact of the Global Attention Mechanism (GAM) and hyperparameter optimization on the performance of the YOLO11
Satyo Widijanuarto, Ema Utami
doaj +1 more source
Non-stochastic Best Arm Identification and Hyperparameter Optimization [PDF]
Kevin Jamieson, Ameet Talwalkar
openalex +1 more source
Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu +5 more
wiley +1 more source
Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions.
Christos Ch. Andrianos +6 more
doaj +1 more source
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initialization, limiting practitioners' ability to influence the optimization process as new insights emerge. This limits the applicability of BO in iterative machine learning development
Lukas Fehring +5 more
openaire +2 more sources
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 +8 more
wiley +1 more source
A Hybrid Optimization Model for Transformer Fault Diagnosis Based on Gas Classification
Dissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters.
Junju Lai +6 more
doaj +1 more source
Gradient-based Hyperparameter Optimization Over Long Horizons [PDF]
Paul Micaelli, Amos Storkey
openalex +1 more source

