Results 81 to 90 of about 109,092 (251)
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
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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
Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength
BuildingsThis study assesses the impact of hyperparameter optimization algorithms on the performance of machine learning-based concrete compressive strength prediction models.
Jeonghyun Kim, Donwoo Lee
doaj +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
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Scaling Laws for Hyperparameter Optimization
, 2023 Accepted at NeurIPS ...
Kadra, Arlind +3 more
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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
Scientific ReportsGiven that the decision tree C4.5 algorithm has outstanding performance in prediction accuracy on medical datasets and is highly interpretable, this paper carries out an optimization study on the selection of hyperparameters of the algorithm in order to ...
Yiyan Zhang, Yi Xin, Qin Li
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An Optimized Hyperparameter Tuning for Improved Hate Speech Detection with Multilayer Perceptron
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)Hate speech classification is a critical task in the domain of natural language processing, aiming to mitigate the negative impacts of harmful content on digital platforms.
Muhamad Ridwan, Ema Utami
doaj +1 more source
IEEE Access, 2018 Recently, emotion recognition using low-cost wearable sensors based on electroencephalogram and blood volume pulse has received much attention. Long short-term memory (LSTM) networks, a special type of recurrent neural networks, have been applied ...
Bahareh Nakisa +4 more
doaj +1 more source
Stochastic Hyperparameter Optimization through Hypernetworks
, 2018 Machine learning models are often tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of weights and hyperparameters. Our process trains a neural network to output approximately optimal weights as a function of hyperparameters.
Lorraine, Jonathan, Duvenaud, David
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