Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames
This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant b…
AutoMLBayesian OptimizationClassificationHyperparameter Optimization+2Similarity search on neighbor's graphs with automatic Pareto optimal performance and minimum expected quality setups based on hyperparameter optimization
This manuscript introduces an autotuned algorithm for searching nearest neighbors based on neighbor graphs and optimization metaheuristics to produce Pareto-optimal searches for quality and search speed automatically; th…
Hyperparameter OptimizationDiscrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters
Machine learning (ML) methods are used in most technical areas such as image recognition, product recommendation, financial analysis, medical diagnosis, and predictive maintenance. An important aspect of implementing ML …
BIG-bench Machine LearningFinancial AnalysisHyperparameter Optimizationimage-classification+5Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm
In recent years, people from all over the world are suffering from one of the most severe diseases in history, known as Coronavirus disease 2019, COVID-19 for short. When the virus reaches the lungs, it has a higher prob…
Hyperparameter Optimizationimage-classificationImage ClassificationX-ray ClassificationOnline Calibrated and Conformal Prediction Improves Bayesian Optimization
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization. However, these estimates can be imperfect if the data violates assumptions made by the model (e.…
Bayesian OptimizationConformal PredictionDecision MakingHyperparameter Optimization+1Evaluating Generic Auto-ML Tools for Computational Pathology
Image analysis tasks in computational pathology are commonly solved using convolutional neural networks (CNNs). The selection of a suitable CNN architecture and hyperparameters is usually done through exploratory iterati…
AutoMLClassificationHyperparameter OptimizationBenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarkin…
BenchmarkingHyperparameter OptimizationregressionAutomated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks. The process of designing HPO algorithms, however, is still an unsystem…
Bayesian OptimizationHyperparameter OptimizationA survey on multi-objective hyperparameter optimization algorithms for Machine Learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizi…
BIG-bench Machine LearningHyperparameter OptimizationDynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
Limited computational budgets often prevent transformers from being used in production and from having their high accuracy utilized. TinyBERT addresses the computational efficiency by self-distilling BERT into a smaller …
Computational EfficiencyHyperparameter OptimizationQuestion AnsweringA Simple and Fast Baseline for Tuning Large XGBoost Models
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can further improve the predictive performance, …
Hyperparameter OptimizationSearching in the Forest for Local Bayesian Optimization
Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO). Recent empirical experiments s…
Bayesian OptimizationHyperparameter OptimizationThe Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there …
BIG-bench Machine LearningHyperparameter OptimizationImportance of Kernel Bandwidth in Quantum Machine Learning
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is expec…
BIG-bench Machine LearningHyperparameter OptimizationInductive BiasQuantum Machine LearningPersonalized Benchmarking with the Ludwig Benchmarking Toolkit
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmark models across tasks and objectives of …
BenchmarkingHyperparameter Optimizationtext-classificationText ClassificationExplaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models. However, there is often a lack of valuable insights into the effects of different hyperparamete…
Bayesian OptimizationBIG-bench Machine LearningHyperparameter OptimizationInterpretable Machine LearningLassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its co…
Bayesian OptimizationHyperparameter OptimizationregressionVocal Bursts Intensity PredictionMeta-Learning to Improve Pre-Training
Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains. PT can incorporate various design choices such as t…
Data AugmentationHyperparameter OptimizationMeta-LearningAutomated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2). The competition organizers provide an…
Bayesian OptimizationHyperparameter OptimizationConcepts for Automated Machine Learning in Smart Grid Applications
Undoubtedly, the increase of available data and competitive machine learning algorithms has boosted the popularity of data-driven modeling in energy systems. Applications are forecasts for renewable energy generation and…
Autonomous VehiclesBIG-bench Machine LearningFeature EngineeringHyperparameter Optimization