Results 91 to 100 of about 127,719 (261)
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Frontiers in OncologyBrain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span.
Muhammad Sami Ullah +5 more
doaj +1 more source
Hyperparameter Optimization: A Spectral Approach
, 2017 We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Hazan, Elad, Klivans, Adam, Yuan, Yang
openaire +2 more sources
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 +6 more
wiley +1 more source
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
, 2017 Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
Bartels, Simon +4 more
core
Hyperparameter Optimization with Differentiable Metafeatures
, 2021 Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to measure the similarity between datasets, leading to a better initialization of HPO models.
Jomaa, Hadi S. +2 more
openaire +2 more sources
Advanced Materials Interfaces, EarlyView.SAA significantly enhanced Al/PU bonding, increasing SLSS by up to 920% and fracture energy by 15 100% through optimized micro‐nano porous surfaces. RSM identified the optimal anodizing conditions, while ML confirmed sulfuric acid concentration and roughness as dominant predictors of strength.
Umut Bakhbergen +6 more
wiley +1 more source
HyperSpace: Distributed Bayesian Hyperparameter Optimization
2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2018 As machine learning models continue to increase in complexity, so does the potential number of free model parameters commonly known as hyperparameters. While there has been considerable progress toward finding optimal configurations of these hyperparameters, many optimization procedures are treated as black boxes. We believe optimization methods should
M. Todd Young +3 more
openaire +2 more sources
Scaling Laws for Hyperparameter Optimization
, 2023 Accepted at NeurIPS ...
Kadra, Arlind +3 more
openaire +2 more sources
Advanced Materials Technologies, EarlyView.This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding +5 more
wiley +1 more source