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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 176–200 of 813 papers

TitleStatusHype
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—0
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in TransformersCode0
Simple Hack for Transformers against Heavy Long-Text Classification on a Time- and Memory-Limited GPU Service—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties—0
Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship TablesCode0
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis—0
Adaptive Hyperparameter Optimization for Continual Learning Scenarios—0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
Hyperparameter Tuning MLPs for Probabilistic Time Series ForecastingCode0
A machine learning workflow to address credit default prediction—0
Statistical Mechanics of Dynamical System Identification—0
Transformers for Low-Resource Languages:Is Féidir Linn!—0
Parallel Hyperparameter Optimization Of Spiking Neural NetworkCode0
Exploratory Landscape Analysis for Mixed-Variable Problems—0
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization—0
Universal Link Predictor By In-Context Learning on Graphs—0
Glocal Hypergradient Estimation with Koopman Operator—0
Poisson Process for Bayesian Optimization—0
Breaking MLPerf Training: A Case Study on Optimizing BERT—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble—0
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