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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 751–775 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
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