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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 51–100 of 813 papers

TitleStatusHype
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Adapters Strike BackCode1
Multi-Objective Population Based TrainingCode1
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Implicit differentiation of Lasso-type models for hyperparameter optimizationCode1
Kronecker Decomposition for Knowledge Graph EmbeddingsCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
Online hyperparameter optimization by real-time recurrent learningCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Online Learning Rate Adaptation with Hypergradient DescentCode1
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
GPT Takes the Bar ExamCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic AlgorithmCode1
HyperNOs: Automated and Parallel Library for Neural Operators ResearchCode1
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for LassoCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
Random Error Sampling-based Recurrent Neural Network Architecture OptimizationCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
Generative Adversarial Neural OperatorsCode1
A Critical Assessment of State-of-the-Art in Entity AlignmentCode1
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Hyperparameter Importance Across DatasetsCode1
Hyperparameter optimization in deep multi-target predictionCode1
Hyperparameter Optimization via Sequential Uniform DesignsCode1
A Three-regime Model of Network PruningCode1
Automated Machine Learning in InsuranceCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?Code1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
Efficient Hyperparameter Optimization for Differentially Private Deep LearningCode1
Evolutionary Neural AutoML for Deep LearningCode1
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