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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 71–80 of 813 papers

TitleStatusHype
Benchmarking YOLOv8 for Optimal Crack Detection in Civil Infrastructure—0
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting ModelsCode0
HyperQ-Opt: Q-learning for Hyperparameter Optimization—0
Bilevel Learning with Inexact Stochastic GradientsCode0
Automated Image Captioning with CNNs and TransformersCode0
Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization—0
Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence—0
Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach—0
Machine learning approach for mapping the stable orbits around planets—0
Hyperparameter Tuning Through Pessimistic Bilevel Optimization—0
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