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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
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
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
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
AutoML: A Survey of the State-of-the-ArtCode1
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic AlgorithmCode1
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter OptimizationCode1
Kronecker Decomposition for Knowledge Graph EmbeddingsCode1
A Three-regime Model of Network PruningCode1
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
Automated Machine Learning in InsuranceCode1
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Meta-Surrogate Benchmarking for Hyperparameter OptimizationCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
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