Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Enhancing Performance and Calibration in Quantile Hyperparameter Optimization
Bayesian hyperparameter optimization relies heavily on Gaussian Process (GP) surrogates, due to robust distributional posteriors and strong performance on limited training samples. GPs however underperform in categorical…
Hyperparameter OptimizationGRPOformer: Advancing Hyperparameter Optimization via Group Relative Policy Optimization
Hyperparameter optimization (HPO) plays a critical role in improving model performance. Transformer-based HPO methods have shown great potential; however, existing approaches rely heavily on large-scale historical optimi…
Hyperparameter OptimizationReinforcement LearningShort-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
This study presents the development and optimization of a deep learning model based on Long Short-Term Memory (LSTM) networks to predict short-term hourly electricity demand in Córdoba, Argentina. Integrating historical …
Hyperparameter OptimizationBenchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
This study compares the Boundary Element Method (BEM) and Physics-Informed Neural Networks (PINNs) for solving the two-dimensional Helmholtz equation in wave scattering problems. The objective is to evaluate the performa…
Hyperparameter OptimizationDemandLens: Enhancing Forecast Accuracy Through Product-Specific Hyperparameter Optimization
DemandLens demonstrates an innovative Prophet based forecasting model for the mattress-in-a-box industry, incorporating COVID-19 metrics and SKU-specific hyperparameter optimization. This industry has seen significant gr…
Hyperparameter OptimizationMetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Effective model and hyperparameter selection remains a major challenge in deep learning, often requiring extensive expertise and computation. While AutoML and large language models (LLMs) promise automation, current LLM-…
Hyperparameter OptimizationEvaluating the Efficiency of Latent Spaces via the Coupling-Matrix
A central challenge in representation learning is constructing latent embeddings that are both expressive and efficient. In practice, deep networks often produce redundant latent spaces where multiple coordinates encode …
Hyperparameter OptimizationNeural Architecture SearchRepresentation LearningChallenges in Deep Learning-Based Small Organ Segmentation: A Benchmarking Perspective for Medical Research with Limited Datasets
Accurate segmentation of carotid artery structures in histopathological images is vital for cardiovascular disease research. This study systematically evaluates ten deep learning segmentation models including classical a…
Hyperparameter OptimizationData AugmentationDomain Adaptation of LLMs for Process Data
In recent years, Large Language Models (LLMs) have emerged as a prominent area of interest across various research domains, including Process Mining (PM). Current applications in PM have predominantly centered on prompt …
parameter-efficient fine-tuningHyperparameter OptimizationPrompt EngineeringDomain AdaptationSurrogate Benchmarks for Model Merging Optimization
Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the performance of the merged model. Because s…
Hyperparameter OptimizationTune My Adam, Please!
The Adam optimizer remains one of the most widely used optimizers in deep learning, and effectively tuning its hyperparameters is key to optimizing performance. However, tuning can be tedious and costly. Freeze-thaw Baye…
Hyperparameter OptimizationQuickly Tuning Foundation Models for Image Segmentation
Foundation models like SAM (Segment Anything Model) exhibit strong zero-shot image segmentation performance, but often fall short on domain-specific tasks. Fine-tuning these models typically requires significant manual e…
Hyperparameter OptimizationImage SegmentationImproving Liver Disease Diagnosis with SNNDeep: A Custom Spiking Neural Network Using Diverse Learning Algorithms
Purpose: Spiking neural networks (SNNs) have recently gained attention as energy-efficient, biologically plausible alternatives to conventional deep learning models. Their application in high-stakes biomedical imaging re…
Hyperparameter OptimizationBinary ClassificationActive LearningAdaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals
Accurate prediction of hydrogen sorption in clays, shales, and coals is vital for advancing underground hydrogen storage, natural hydrogen exploration, and radioactive waste containment. Traditional experimental methods,…
Hyperparameter OptimizationFeature EngineeringReinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
The growing adoption of home banking systems has increased cyberfraud risks, requiring detection models that are accurate, fair, and explainable. While AI methods show promise, they face challenges including computationa…
Hyperparameter OptimizationReinforcement LearningFraud DetectionSuccessive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
Successive Halving is a popular algorithm for hyperparameter optimization which allocates exponentially more resources to promising candidates. However, the algorithm typically relies on intermediate performance values t…
Hyperparameter OptimizationGaussian ProcessesIn-Context Decision Making for Optimizing Complex AutoML Pipelines
Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the advancements of pre-trained models, modern ML workflows go beyond hyperparameter o…
Hyperparameter OptimizationDecision MakingHierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models
Demand forecasting in competitive, uncertain business environments requires models that can integrate multiple evaluation perspectives rather than being restricted to hyperparameter optimization based on a single metric.…
Hyperparameter OptimizationADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Determining the optimal data mixture for large language model training remains a challenging problem with an outsized impact on performance. In practice, language model developers continue to rely on heuristic exploratio…
Hyperparameter OptimizationEmpirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains
This paper examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of …
Hyperparameter OptimizationTime Series ClassificationTime Series Prediction