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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 701–750 of 813 papers

TitleStatusHype
Deep Neural Network Hyperparameter Optimization with Orthogonal Array TuningCode0
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality MetricsCode0
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct searchCode0
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter OptimizationCode0
Hyp-RL : Hyperparameter Optimization by Reinforcement LearningCode0
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design—0
Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence NetworksCode0
LambdaOpt: Learn to Regularize Recommender Models in Finer LevelsCode0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Sequential Gaussian Processes for Online Learning of Nonstationary FunctionsCode0
DEEP-BO for Hyperparameter Optimization of Deep NetworksCode0
Evolving Rewards to Automate Reinforcement Learning—0
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization—0
Tabular Benchmarks for Joint Architecture and Hyperparameter OptimizationCode0
Exploring the Hyperparameter Landscape of Adversarial Robustness—0
Optimizing for Generalization in Machine Learning with Cross-Validation Gradients—0
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity—0
Hyperparameter Optimization in Black-box Image Processing using Differentiable ProxiesCode0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
sharpDARTS: Faster and More Accurate Differentiable Architecture SearchCode0
Web Links Prediction And Category-Wise Recommendation Based On Browser HistoryCode0
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelinesCode0
Random Search and Reproducibility for Neural Architecture SearchCode0
How to "DODGE" Complex Software Analytics?—0
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metricCode0
Instance-Level Microtubule Tracking—0
Recombination of Artificial Neural Networks—0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Katib: A Distributed General AutoML Platform on Kubernetes—0
Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature ParametersCode0
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features—0
Scalable Hyperparameter Transfer Learning—0
Private Selection from Private Candidates—0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO—0
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates—0
Deep Genetic Network—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Preprocessor Selection for Machine Learning Pipelines—0
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms—0
Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components—0
Is One Hyperparameter Optimizer Enough?—0
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks—0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Automatic Gradient BoostingCode0
A Tutorial on Bayesian OptimizationCode0
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-LearningCode0
Bilevel Programming for Hyperparameter Optimization and Meta-Learning—0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning—0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
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