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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 676–700 of 813 papers

TitleStatusHype
Single Headed Attention RNN: Stop Thinking With Your HeadCode0
A Simple Heuristic for Bayesian Optimization with A Low Budget—0
A Neural Network Based on the Johnson S_U Translation System and Related Application to Electromyogram Classification—0
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics—0
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter TuningCode0
Prior Specification for Bayesian Matrix Factorization via Prior Predictive MatchingCode0
Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem—0
MARTHE: Scheduling the Learning Rate Via Online HypergradientsCode0
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning—0
Constrained Bayesian Optimization with Max-Value Entropy Search—0
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter SettingsCode0
Mental Task Classification Using Electroencephalogram SignalCode0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Towards modular and programmable architecture searchCode0
Gradient Descent: The Ultimate OptimizerCode0
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning—0
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods—0
Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm—0
Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space—0
A scalable constructive algorithm for the optimization of neural network architectures—0
Transferable Neural Processes for Hyperparameter Optimization—0
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of HyperparametersCode0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
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