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Hyperparameter Optimization

Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

Papers

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Showing 726–750 of 813 papers

TitleStatusHype
Instance-Level Microtubule Tracking—0
Recombination of Artificial Neural Networks—0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Katib: A Distributed General AutoML Platform on Kubernetes—0
Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature ParametersCode0
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features—0
Scalable Hyperparameter Transfer Learning—0
Private Selection from Private Candidates—0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO—0
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates—0
Deep Genetic Network—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Preprocessor Selection for Machine Learning Pipelines—0
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms—0
Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components—0
Is One Hyperparameter Optimizer Enough?—0
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks—0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Automatic Gradient BoostingCode0
A Tutorial on Bayesian OptimizationCode0
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-LearningCode0
Bilevel Programming for Hyperparameter Optimization and Meta-Learning—0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning—0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
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