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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

Showing 726750 of 813 papers

TitleStatusHype
Benchmarking Automatic Machine Learning FrameworksCode3
Is One Hyperparameter Optimizer Enough?0
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks0
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-Learning0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction0
Optimizing for Generalization in Machine Learning with Cross-Validation GradientsCode0
Holarchic Structures for Decentralized Deep Learning - A Performance Analysis0
Rafiki: Machine Learning as an Analytics Service SystemCode0
Scalable Factorized Hierarchical Variational Autoencoder TrainingCode0
An LP-based hyperparameter optimization model for language modeling0
Best arm identification in multi-armed bandits with delayed feedback0
Natural Gradient Deep Q-learning0
Reviving and Improving Recurrent Back-PropagationCode0
Autostacker: A Compositional Evolutionary Learning System0
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 Learning0
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