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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 701–750 of 813 papers

TitleStatusHype
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response FunctionsCode1
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelinesCode0
Web Links Prediction And Category-Wise Recommendation Based On Browser HistoryCode0
Random Search and Reproducibility for Neural Architecture SearchCode0
Evolutionary Neural AutoML for Deep LearningCode1
How to "DODGE" Complex Software Analytics?—0
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metricCode0
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
The Neural Hype and Comparisons Against Weak BaselinesCode2
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
A System for Massively Parallel Hyperparameter TuningCode1
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
Benchmarking Automatic Machine Learning FrameworksCode3
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
BOHB: Robust and Efficient Hyperparameter Optimization at ScaleCode1
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
Optimizing for Generalization in Machine Learning with Cross-Validation GradientsCode0
Holarchic Structures for Decentralized Deep Learning - A Performance Analysis—0
Rafiki: Machine Learning as an Analytics Service SystemCode0
Scalable Factorized Hierarchical Variational Autoencoder TrainingCode0
An LP-based hyperparameter optimization model for language modeling—0
Best arm identification in multi-armed bandits with delayed feedback—0
Natural Gradient Deep Q-learning—0
Reviving and Improving Recurrent Back-PropagationCode0
Autostacker: A Compositional Evolutionary Learning System—0
Stochastic Hyperparameter Optimization through HypernetworksCode1
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
Practical Transfer Learning for Bayesian OptimizationCode0
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning—0
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