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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 126–150 of 813 papers

TitleStatusHype
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
Implicit differentiation of Lasso-type models for hyperparameter optimizationCode1
Hyperparameter optimization in deep multi-target predictionCode1
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
Hyperparameter Optimization via Sequential Uniform DesignsCode1
Improving Fast Minimum-Norm Attacks with Hyperparameter OptimizationCode1
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing SystemsCode1
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
Hyperparameter Importance Across DatasetsCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
BOHB: Robust and Efficient Hyperparameter Optimization at ScaleCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
Enabling hyperparameter optimization in sequential autoencoders for spiking neural dataCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
Improving Hyperparameter Optimization with Checkpointed Model WeightsCode1
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
PriorBand: Practical Hyperparameter Optimization in the Age of Deep LearningCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
HyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural NetworksCode0
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct searchCode0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
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