Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep Shift Neural Networks (D…
Hyperparameter OptimizationImage ClassificationComparative Evaluation of Machine Learning and Deep Learning Models for Wound-Rotor Synchronous Motor Performance Prediction
Wound rotor synchronous motors have emerged as a strong alternative that eliminates dependence on REEs. However, WRSM design requires the simultaneous optimization of numerous geometric and electromagnetic parameters, an…
Hyperparameter OptimizationWhen Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model
Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations. We test that claim und…
Hyperparameter OptimizationEvolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training. However, their performance suffers from unstable convergence, training plateau…
Hyperparameter OptimizationShort-Term Electricity Demand Forecasting for New England Using a Hybrid Transformer-XGBoost Framework with Weather, Calendar, and COVID-19 Indicators
Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization. This paper presents a hybrid framework combining a Transformer …
Hyperparameter OptimizationModeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection
Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity. Many deep-learning methods performing well on simulated data …
Hyperparameter OptimizationEvaluation of AutoML Frameworks for IDS under Imbalanced Data Conditions of the NSL-KDD Dataset
This work investigates the impact of severe class imbalance on the performance of automated machine learning (AutoML) frameworks for multiclass network intrusion detection using the NSL-KDD dataset. Unlike previous studi…
Network Intrusion DetectionHyperparameter OptimizationBinary ClassificationEnsemble LearningA Systematic Approach for Selecting Trajectories for Data Augmentation
Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior …
Hyperparameter OptimizationData AugmentationSmall Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining
Short pretraining runs can reduce experimental cost, but they can also over-promote configurations that only look strong at tiny budgets. We study an auditable staged-promotion protocol for a fixed micro-pretraining runn…
Hyperparameter OptimizationImportance-Aware Scheduling for High-Dimensional Hyperparameter Optimization
Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimensional spaces where evaluations are costly and progress is diluted across …
Hyperparameter OptimizationA Framework for Evaluating and Benchmarking Concept Drift Detection Methods
Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, progress in the field is hindered by inco…
Hyperparameter OptimizationDepth over Fidelity in Fixed-Budget Noisy Evolution Strategies
Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute. W…
Hyperparameter OptimizationHyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network
Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes. Latent factorization of tensors (LFT) extracts target knowledge from DWDNs via low-rank embedding. Ho…
Hyperparameter OptimizationRepresentation LearningOptuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE
Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis. While c-TPE proposes an expected constrained improvement (ECI) approach assuming i…
Hyperparameter OptimizationProvably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency. Existing methods use pr…
Hyperparameter OptimizationBBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning
The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets. Deep unsupervised learning architectures, particularly Autoencoders (AEs), are increasingly used for dimensionality r…
Hyperparameter OptimizationDimensionality ReductionRepresentation LearningTransfer LearningLLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems require the joint optimization of highly coupl…
Hyperparameter OptimizationQuestion AnsweringEfficient Hyperparameter Optimization for LLM Reinforcement Learning
Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive. Existing multi-fidelity…
Hyperparameter OptimizationComputational EfficiencyReinforcement LearningHow Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-st…
Hyperparameter OptimizationFew-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network
This work proposes a novel solution to predict pulsar timing residuals with limited data, addressing the critical challenge of data scarcity across spin-frequency subgroups of millisecond pulsars in PTA datasets. The pro…
Hyperparameter Optimization