Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
In predictive maintenance, early and late RUL prediction errors carry asymmetric consequences, yet hyperparameter optimization typically targets a single accuracy metric that treats both directions equally. This study tr…
Hyperparameter OptimizationAn Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within …
Hyperparameter OptimizationFeature ImportanceInvestigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in developing neural networks. However, their effectiveness heavily depend…
Hyperparameter OptimizationDistributional Extrapolation for Interactions
Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We study combinatorial extrapolation, wher…
Hyperparameter OptimizationDrug DiscoveryTRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH
Combined algorithm selection and hyperparameter optimization (CASH) searches a conditional space in which the selected model determines which hyperparameters are active. In time-series forecasting, temporal choices, chro…
Hyperparameter OptimizationReinforcement LearningOn the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via vario…
Hyperparameter OptimizationGaussian ProcessesInterpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was establish…
Interpretable Machine LearningHyperparameter OptimizationUsing Data-Derived Priors to Guide CNN Architecture Design for NIR Chemometrics
Convolutional neural networks (CNN) for near-infrared (NIR) chemometrics are often designed using generic architectural rules, although spectral datasets differ in sampling, smoothness, redundancy, and sample size. We te…
Hyperparameter OptimizationRevisiting data-driven dynamic security assessment with a tabular foundation model
Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches face two limitations. First, they require…
Hyperparameter OptimizationTransfer LearningA Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for dema…
Hyperparameter OptimizationCompositional Context Fine-Tuning Vision-Language Model for Complex Assembly Action Understanding from Videos
Assembly action understanding is a key enabler for effective human-robot collaborative assembly, yet it remains challenging due to subtle motions and fine-grained hand-object interactions. We adapt vision-language models…
Hyperparameter OptimizationAction UnderstandingMulti-Task LearningAction RecognitionModernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems
Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-mo…
Hyperparameter OptimizationSharper Analysis of Single-Loop Methods for Bilevel Optimization
Bilevel optimization underpins many machine learning applications, including hyperparameter optimization, meta-learning, neural architecture search, and reinforcement learning. While hypergradient-based methods have adva…
Hyperparameter OptimizationNeural Architecture SearchReinforcement LearningBilevel OptimizationOptimizing Large Language Models for Causality Assessment in Pharmacovigilance: Developing a Performance Metric as Objective for Bayesian Hyperparameter Optimization
Background: Growing individual case safety report (ICSR) volumes have intensified demand for scalable automated causality assessment. Large Language Models (LLMs) show promise, yet performance on clinically demanding tas…
Hyperparameter OptimizationEvolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous Steering
This research investigates the optimization of Convolutional and Dense Neural Networks (CNNs and DNNs) for autonomous steering using the (N+M) Evolution Strategy (ES) with the 1/5th success rule. The primary objective is…
Hyperparameter OptimizationComputational EfficiencyAutonomous DrivingAdaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction
Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets. The model incorporates a Market Regime Encoder, an Adaptive Gate Network, and an Adaptive Financial Con…
Hyperparameter OptimizationBilevel Optimization for Neural Architecture Search
Bilevel optimization has become an influential and widely adopted framework for addressing hierarchical optimization problems in machine learning, providing an effective approach to modeling the interaction between two l…
Hyperparameter OptimizationNeural Architecture SearchBilevel OptimizationTransformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS
High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be scre…
Hyperparameter OptimizationIncremental LearningActive LearningASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments
Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget. Because every HPO tool…
Hyperparameter OptimizationExplainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling
Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The current literature tends to ignore the use of AI-based mental health…
Hyperparameter OptimizationSleep Quality