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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 26–50 of 813 papers

TitleStatusHype
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language ModelsCode2
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random FeaturesCode2
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian OptimizationCode2
AutoML: A Survey of the State-of-the-ArtCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Evolutionary Neural AutoML for Deep LearningCode1
Fast Optimizer BenchmarkCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Automated Machine Learning in InsuranceCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?Code1
Deep Pipeline Embeddings for AutoMLCode1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
Adapters Strike BackCode1
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