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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 76–100 of 813 papers

TitleStatusHype
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
Random Error Sampling-based Recurrent Neural Network Architecture OptimizationCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
GPT Takes the Bar ExamCode1
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scaleCode1
A Critical Assessment of State-of-the-Art in Entity AlignmentCode1
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
BOHB: Robust and Efficient Hyperparameter Optimization at ScaleCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Kronecker Decomposition for Knowledge Graph EmbeddingsCode1
A Three-regime Model of Network PruningCode1
AutoML: A Survey of the State-of-the-ArtCode1
Generative Adversarial Neural OperatorsCode1
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic AlgorithmCode1
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
Automated Machine Learning in InsuranceCode1
Flexible Differentiable Optimization via Model TransformationsCode1
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
Multi-Objective Population Based TrainingCode1
Fast Optimizer BenchmarkCode1
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