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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 126–150 of 813 papers

TitleStatusHype
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Optimizing Millions of Hyperparameters by Implicit DifferentiationCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Random Error Sampling-based Recurrent Neural Network Architecture OptimizationCode1
Enabling hyperparameter optimization in sequential autoencoders for spiking neural dataCode1
AutoML: A Survey of the State-of-the-ArtCode1
Optuna: A Next-generation Hyperparameter Optimization FrameworkCode1
Meta-Surrogate Benchmarking for Hyperparameter OptimizationCode1
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response FunctionsCode1
Evolutionary Neural AutoML for Deep LearningCode1
A System for Massively Parallel Hyperparameter TuningCode1
BOHB: Robust and Efficient Hyperparameter Optimization at ScaleCode1
Stochastic Hyperparameter Optimization through HypernetworksCode1
High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsCode1
Hyperparameter Importance Across DatasetsCode1
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
Online Learning Rate Adaptation with Hypergradient DescentCode1
Forward and Reverse Gradient-Based Hyperparameter OptimizationCode1
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
Overtuning in Hyperparameter OptimizationCode0
Quantum-Classical Hybrid Quantized Neural Network—0
CBTOPE2: An improved method for predicting of conformational B-cell epitopes in an antigen from its primary sequence—0
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