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Hyperparameter Optimization

78 papers with code ยท Methodology
Subtask of AutoML

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Weighted Random Search for Hyperparameter Optimization

3 Apr 2020

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms.

HYPERPARAMETER OPTIMIZATION

Weighted Random Search for CNN Hyperparameter Optimization

30 Mar 2020

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters).

HYPERPARAMETER OPTIMIZATION

Model-based Asynchronous Hyperparameter Optimization

24 Mar 2020

We introduce a model-based asynchronous multi-fidelity hyperparameter optimization (HPO) method, combining strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.

HYPERPARAMETER OPTIMIZATION IMAGE CLASSIFICATION LANGUAGE MODELLING

PHS: A Toolbox for Parallel Hyperparameter Search

26 Feb 2020

We introduce an open source python framework named PHS - Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function.

HYPERPARAMETER OPTIMIZATION

Implicit differentiation of Lasso-type models for hyperparameter optimization

20 Feb 2020

Our approach scales to high-dimensional data by leveraging the sparsity of the solutions.

HYPERPARAMETER OPTIMIZATION

Multi-Task Multicriteria Hyperparameter Optimization

15 Feb 2020

The article presents optimal hyperparameters for various criteria significance coefficients.

HYPERPARAMETER OPTIMIZATION IMAGE CLASSIFICATION

Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization

11 Feb 2020

Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization.

COMBINATORIAL OPTIMIZATION HYPERPARAMETER OPTIMIZATION TRANSFER LEARNING

Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications

10 Feb 2020

A traditional artificial neural network (ANN) is normally trained slowly by a gradient descent algorithm, such as the backpropagation algorithm, since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs.

HYPERPARAMETER OPTIMIZATION

PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces

24 Jan 2020

Traditionally, an artificial neural network (ANN) is trained slowly by a gradient descent algorithm such as the backpropagation algorithm since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs.

HYPERPARAMETER OPTIMIZATION