Results 81 to 90 of about 1,925,652 (255)

Score model network: Optimization hyperparameters.

open access: yes
Score model network: Optimization hyperparameters.
Christopher Frederik Blum (17972603)   +4 more
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

open access: yesAdvanced Materials, EarlyView.
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

MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning

open access: yesFrontiers in Plant Science
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

open access: yesCoRR, 2018
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

open access: yes, 2022
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  

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

open access: yesAdvanced Materials, EarlyView.
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

open access: yesJ. Mach. Learn. Res., 2019
ISSN:1532 ...
Felix Berkenkamp   +2 more
openaire   +5 more sources

Selected backbone-specific optimization and loss hyperparameters.

open access: yes
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

open access: yes, 2021
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

open access: yesAdvanced Materials, EarlyView.
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

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