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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 751–800 of 813 papers

TitleStatusHype
AMLA: an AutoML frAmework for Neural Network Design—0
Online Hyper-Parameter Optimization—0
A Bridge Between Hyperparameter Optimization and Learning-to-learnCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing—0
Are GANs Created Equal? A Large-Scale StudyCode0
Transfer Learning to Learn with Multitask Neural Model Search—0
Learning to Warm-Start Bayesian Hyperparameter Optimization—0
Hyperparameter Importance Across DatasetsCode1
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded ModesCode3
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization—0
Open Loop Hyperparameter Optimization and Determinantal Point Processes—0
Hyperparameter Optimization: A Spectral ApproachCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
An effective algorithm for hyperparameter optimization of neural networks—0
DeepArchitect: Automatically Designing and Training Deep ArchitecturesCode0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates—0
Online Learning Rate Adaptation with Hypergradient DescentCode1
Online Convex Optimization with Unconstrained Domains and Losses—0
Global optimization of Lipschitz functionsCode0
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
Large-Scale Evolution of Image ClassifiersCode0
RoBO: A Flexible and Robust Bayesian Optimization Framework in PythonCode0
Google Vizier: A Service for Black-Box OptimizationCode0
Bayesian Optimization with Robust Bayesian Neural NetworksCode0
Scalable Hyperparameter Optimization with Products of Gaussian Process ExpertsCode0
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models—0
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training—0
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF SurrogatesCode0
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large DatasetsCode0
Bayesian Hyperparameter Optimization for Ensemble Learning—0
Evaluation System for a Bayesian Optimization Service—0
CMA-ES for Hyperparameter Optimization of Deep Neural Networks—0
A Stratified Analysis of Bayesian Optimization Methods—0
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Hyperparameter optimization with approximate gradientCode0
Automating biomedical data science through tree-based pipeline optimizationCode0
Efficient and Robust Automated Machine LearningCode3
Scalable Gradient-Based Tuning of Continuous Regularization HyperparametersCode0
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
No Regret Bound for Extreme Bandits—0
Learning Structural Kernels for Natural Language Processing—0
apsis - Framework for Automated Optimization of Machine Learning Hyper ParametersCode0
Non-stochastic Best Arm Identification and Hyperparameter OptimizationCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Gradient-based Hyperparameter Optimization through Reversible LearningCode0
Supplementary Material for Efficient and Robust Automated Machine LearningCode3
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