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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 626–650 of 813 papers

TitleStatusHype
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
PHS: A Toolbox for Parallel Hyperparameter SearchCode0
Implicit differentiation of Lasso-type models for hyperparameter optimizationCode1
Multi-Task Multicriteria Hyperparameter Optimization—0
PHOTONAI -- A Python API for Rapid Machine Learning Model Development—0
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications—0
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
Extreme Algorithm Selection With Dyadic Feature RepresentationCode0
Hyperparameter Optimization for Forecasting Stock Returns—0
PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces—0
Optimization of Convolutional Neural Network Using the Linearly Decreasing Weight Particle Swarm Optimization—0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization—0
Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems—0
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles—0
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS—0
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?—0
Improved Covariance Matrix Estimator using Shrinkage Transformation and Random Matrix Theory—0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning—0
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
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