Results 81 to 90 of about 1,925,652 (255)
Score model network: Optimization hyperparameters.
Score model network: Optimization hyperparameters.
Christopher Frederik Blum (17972603) +4 more
core +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often ...
Freddy Mora-Poblete +4 more
doaj +1 more source
Hyperparameter Optimization for Effort Estimation
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia +5 more
openaire +3 more sources
Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
Many machine learning solutions are framed as optimization problems which rely on good hyperparameters. Algorithms for tuning these hyperparameters usually assume access to exact solutions to the underlying learning problem, which is typically not ...
Roberts, Lindon, Ehrhardt, Matthias
core
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh +5 more
wiley +1 more source
No-Regret Bayesian Optimization with Unknown Hyperparameters
ISSN:1532 ...
Felix Berkenkamp +2 more
openaire +5 more sources
Selected backbone-specific optimization and loss hyperparameters.
Selected backbone-specific optimization and loss hyperparameters.
Shafiq Ul Rehman (23115148) +3 more
core +1 more source
An Effective Hyperparameters Optimization Algorithm for Metro Passenger Flow Prediction
An accurate neural network model must rely on suitable model architecture and an appropriate model training process. The hyperparameters optimization problem aims to search for the best neural network architecture in a large solution space and maximize ...
Fang, Zhi-Yan
core
Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health
This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim +4 more
wiley +1 more source

