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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 151–160 of 813 papers

TitleStatusHype
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN—0
Adapters Strike BackCode1
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO—0
Distributional bias compromises leave-one-out cross-validationCode0
Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy—0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
Derivatives of Stochastic Gradient Descent in parametric optimization—0
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs—0
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter OptimizationCode0
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