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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 601–650 of 813 papers

TitleStatusHype
Understanding the effect of hyperparameter optimization on machine learning models for structure design problems—0
On the Iteration Complexity of Hypergradient ComputationCode1
Simple and Scalable Parallelized Bayesian Optimization—0
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Efficient Hyperparameter Optimization under Multi-Source Covariate ShiftCode0
Ranking and benchmarking framework for sampling algorithms on synthetic data streamsCode0
The Statistical Cost of Robust Kernel Hyperparameter Tuning—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs—0
Hyperparameter optimization with REINFORCE and Transformers—0
Semi-supervised Embedding Learning for High-dimensional Bayesian OptimizationCode0
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
HyperSTAR: Task-Aware Hyperparameters for Deep Networks—0
Sherpa: Robust Hyperparameter Optimization for Machine LearningCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
Frugal Optimization for Cost-related HyperparametersCode2
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph EmbeddingsCode1
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment—0
Meta-Learning in Neural Networks: A SurveyCode1
Direct loss minimization algorithms for sparse Gaussian processesCode0
Online Hyperparameter Search Interleaved with Proximal Parameter Updates—0
Weighted Random Search for Hyperparameter OptimizationCode0
Weighted Random Search for CNN Hyperparameter OptimizationCode0
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality—0
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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