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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 101–125 of 813 papers

TitleStatusHype
Online hyperparameter optimization by real-time recurrent learningCode1
Online Hyperparameter Optimization for Class-Incremental LearningCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Evolutionary Neural AutoML for Deep LearningCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
Automated Machine Learning in InsuranceCode1
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
GPT Takes the Bar ExamCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Fast Optimizer BenchmarkCode1
Provably Faster Algorithms for Bilevel OptimizationCode1
Bilevel Optimization: Convergence Analysis and Enhanced DesignCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
AutoML: A Survey of the State-of-the-ArtCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
Flexible Differentiable Optimization via Model TransformationsCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic AlgorithmCode1
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