Results 71 to 80 of about 1,925,652 (255)

Optimizing Hyperparameters with Conformal Quantile Regression

CoRR, 2023
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be ...
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau   +4 more
openaire   +4 more sources

Random Search Plus: A more effective random search for machine learning hyperparameters optimization [PDF]

, 2020
Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization.
Li, Bohan
core  

Destination Prediction Based on Partial Trajectory Data - Hyperparameters

, 2020
This table presents the hyperparameters retrieved after hyperparamter optimization and used to test the final models in "Destination Prediction Based on Partial Trajectory Data"
Patrick Ebel (8349384)
core   +1 more source

Dynamic Observation of Lacy Pattern Formation in a Bone Tissue Model Using Liquid Phase Scanning Transmission Electron Microscopy

Advanced Functional Materials, EarlyView.
Here, we establish a low‐dose liquid‐phase electron microscopy workflow for real‐time observation of biological processes in hydrated conditions. Beam damage is reduced by imaging only 20% of the pixels and reconstructing the complete image using an inpainting algorithm.
Luco Rutten, Avital Wagner, Rona Roverts, Luco Buise, Ben Joosten, Anat Akiva, Elena Macías‐Sánchez, Nico Sommerdijk   +7 more
wiley   +1 more source

Comparison of Particle Swarm Optimization Algorithms in Hyperparameter Optimization Problem of Multi Layered Perceptron

Computer Assisted Methods in Engineering and Science
This paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model.
Kenta Shiomi, Tetsuya Sato, Eisuke Kita
doaj   +1 more source

Optimization-seeking experiment with hyperparameters K.

Optimization-seeking experiment with hyperparameters K.
Xi Zhang (83736), Chunlian Quan (18010703), Qiang Ren (1565044), Fangwei Li (11531956), Shuang Guo (294074), Ying Zhang (40767)   +5 more
core   +1 more source

Replication Data for: eSLP optimization algorithm

, 2023
m-files implementing the eSLP optimization algorithm for the three simulations (3.1. - 3.3.) described in the associated paper. File README.txt contains analytical directions regarding requirements, how to verify the results and videos generated by the ...
Optimization, eSLP
core   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification

Scientific Reports
Land cover and land use (LCLU) information is crucial in different earth observation applications, such as environmental management, infrastructure planning, and urban development.
Pooya Heidari, Asghar Milan
doaj   +1 more source

Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization [PDF]

E3S Web of Conferences
Deep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data.
Dewi Irma Amelia, Rizqullah Muhammad Aldi   +1 more
doaj   +1 more source