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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 51–75 of 813 papers

TitleStatusHype
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
HyperNOs: Automated and Parallel Library for Neural Operators ResearchCode1
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
Adapters Strike BackCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Improving Fast Minimum-Norm Attacks with Hyperparameter OptimizationCode1
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
A Critical Assessment of State-of-the-Art in Entity AlignmentCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
AutoML: A Survey of the State-of-the-ArtCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Automated Machine Learning in InsuranceCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Evolutionary Neural AutoML for Deep LearningCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
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