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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
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Online Hyperparameter Optimization for Class-Incremental LearningCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Evolutionary Neural AutoML for Deep LearningCode1
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
Automated Machine Learning in InsuranceCode1
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
Generative Adversarial Neural OperatorsCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Provably Faster Algorithms for Bilevel OptimizationCode1
Fast Optimizer BenchmarkCode1
PyHopper -- Hyperparameter optimizationCode1
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
AutoML: A Survey of the State-of-the-ArtCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
[Re] Learning Memory Guided Normality for Anomaly DetectionCode1
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
GPT Takes the Bar ExamCode1
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