Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty,…
Bayesian OptimizationHyperparameter OptimizationUncertainty QuantificationAuto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them esp…
Federated LearningHyperparameter OptimizationImage SegmentationLesion Segmentation+6Automated Few-Shot Time Series Forecasting based on Bi-level Programming
New micro-grid design with renewable energy sources and battery storage systems can help improve greenhouse gas emissions and reduce the operational cost. To provide an effective short-/long-term forecasting of both ener…
Decision MakingFew-Shot LearningHyperparameter OptimizationMeta-Learning+3Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-lo…
BIG-bench Machine LearningBilevel OptimizationHyperparameter OptimizationMeta-Learning+2Practitioner Motives to Select Hyperparameter Optimization Methods
Advanced programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sample efficiency in reproducibly finding optimal hyperparameter values of machine learning (ML) models. Yet, ML p…
Bayesian OptimizationBIG-bench Machine LearningHyperparameter OptimizationHyperparameter optimization of data-driven AI models on HPC systems
In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exerc…
Bayesian OptimizationGraph Neural NetworkHyperparameter OptimizationA Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima
Bilevel optimization has found extensive applications in modern machine learning problems such as hyperparameter optimization, neural architecture search, meta-learning, etc. While bilevel problems with a unique inner mi…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningNeural Architecture SearchVisual Speech Recognition for Multiple Languages in the Wild
Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led …
Hyperparameter OptimizationLipreadingspeech-recognitionSpeech Recognition+1DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models
We present two novel hyperparameter optimization strategies for optimization of deep learning models with a modular architecture constructed of multiple subnetworks. As complex networks with multiple subnetworks become m…
Bayesian OptimizationHyperparameter OptimizationShort-answer scoring with ensembles of pretrained language models
We investigate the effectiveness of ensembles of pretrained transformer-based language models on short answer questions using the Kaggle Automated Short Answer Scoring dataset. We fine-tune a collection of popular small,…
Hyperparameter OptimizationSupervising the Multi-Fidelity Race of Hyperparameter Configurations
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods. However, existing methods suffer from a sub-optimal allocation of th…
Bayesian OptimizationGaussian ProcessesHyperparameter OptimizationOne Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constit…
AllBayesian OptimizationDocument ClassificationHyperparameter Optimization+1Random vector functional link network: recent developments, applications, and future directions
Neural networks have been successfully employed in various domains such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural netw…
Hyperparameter OptimizationDimensional criterion for forecasting nonlinear systems by reservoir computing
Reservoir computers (RC) have proven useful as surrogate models in forecasting and replicating systems of chaotic dynamics. The quality of surrogate models based on RCs is crucially dependent on their optimal implementat…
Electroencephalogram (EEG)Hyperparameter OptimizationBilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start
We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction …
Bilevel OptimizationData PoisoningHyperparameter OptimizationMeta-LearningReview of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design proces…
AutoMLFeature EngineeringHyperparameter OptimizationTime Series+2Combined Pruning for Nested Cross-Validation to Accelerate Automated Hyperparameter Optimization for Embedded Feature Selection in High-Dimensional Data with Very Small Sample Sizes
Background: Embedded feature selection in high-dimensional data with very small sample sizes requires optimized hyperparameters for the model building process. For this hyperparameter optimization, nested cross-validatio…
feature selectionHyperparameter OptimizationScaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points. A reformulation of the same GP posterior highlights that this complexity mainly depends on how many \emph{u…
Active LearningHyperparameter OptimizationAdaptive Optimizer for Automated Hyperparameter Optimization Problem
The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjus…
Hyperparameter OptimizationHyperparameter Optimization for COVID-19 Chest X-Ray Classification
Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through pol…
Binary ClassificationClassificationHyperparameter OptimizationX-ray Classification