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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 661–670 of 813 papers

TitleStatusHype
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery—0
Which price to pay? Auto-tuning building MPC controller for optimal economic cost—0
Robust Stability of Gaussian Process Based Moving Horizon Estimation—0
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks—0
A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction—0
Sampling Streaming Data with Parallel Vector Quantization -- PVQ—0
Saturn: Efficient Multi-Large-Model Deep Learning—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization—0
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