Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
Graph Neural Architecture Search (GNAS) facilitates the automatic design of Graph Neural Networks (GNNs) tailored to specific downstream graph learning tasks. However, existing GNAS approaches often require manual adapta…
Feature EngineeringGraph LearningHyperparameter OptimizationLanguage Modeling+3Hyperparameters in Score-Based Membership Inference Attacks
Membership Inference Attacks (MIAs) have emerged as a valuable framework for evaluating privacy leakage by machine learning models. Score-based MIAs are distinguished, in particular, by their ability to exploit the confi…
Hyperparameter OptimizationTransfer LearningqNBO: quasi-Newton Meets Bilevel Optimization
Bilevel optimization, addressing challenges in hierarchical learning tasks, has gained significant interest in machine learning. The practical implementation of the gradient descent method to bilevel optimization encount…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningRenewable Energy Prediction: A Comparative Study of Deep Learning Models for Complex Dataset Analysis
The increasing focus on predicting renewable energy production aligns with advancements in deep learning (DL). The inherent variability of renewable sources and the complexity of prediction methods require robust approac…
Hyperparameter OptimizationL2 RegularizationWhich price to pay? Auto-tuning building MPC controller for optimal economic cost
Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to a…
Bayesian OptimizationComputational EfficiencyHyperparameter OptimizationModel Predictive ControlTutorial: VAE as an inference paradigm for neuroimaging
In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensional datasets such as neuroimaging. By integrating deep learning with Ba…
Bayesian InferenceHyperparameter OptimizationEvaluation of Artificial Intelligence Methods for Lead Time Prediction in Non-Cycled Areas of Automotive Production
The present study examines the effectiveness of applying Artificial Intelligence methods in an automotive production environment to predict unknown lead times in a non-cycle-controlled production area. Data structures ar…
Ensemble LearningHyperparameter OptimizationregressionDataset-Agnostic Recommender Systems
Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tun…
Feature Engineeringfeature selectionHyperparameter OptimizationImputation+2Benchmarking YOLOv8 for Optimal Crack Detection in Civil Infrastructure
Ensuring the structural integrity and safety of bridges is crucial for the reliability of transportation networks and public safety. Traditional crack detection methods are increasingly being supplemented or replaced by …
BenchmarkingHyperparameter OptimizationA Hessian-informed hyperparameter optimization for differential learning rate
Differential learning rate (DLR), a technique that applies different learning rates to different model parameters, has been widely used in deep learning and achieved empirical success via its various forms. For example, …
Hyperparameter Optimizationparameter-efficient fine-tuningA Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require extensive hyperparameter opt…
Hyperparameter OptimizationMambaTime SeriesTime Series ForecastingHyperQ-Opt: Q-learning for Hyperparameter Optimization
Hyperparameter optimization (HPO) is critical for enhancing the performance of machine learning models, yet it often involves a computationally intensive search across a large parameter space. Traditional approaches such…
Bayesian OptimizationHyperparameter OptimizationQ-Learningreinforcement-learning+2Bilevel Learning with Inexact Stochastic Gradients
Bilevel learning has gained prominence in machine learning, inverse problems, and imaging applications, including hyperparameter optimization, learning data-adaptive regularizers, and optimizing forward operators. The la…
Bilevel OptimizationDeblurringDenoisingHyperparameter Optimization+2Automated Image Captioning with CNNs and Transformers
This project aims to create an automated image captioning system that generates natural language descriptions for input images by integrating techniques from computer vision and natural language processing. We employ var…
DescriptiveHyperparameter OptimizationImage CaptioningSpend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization
A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best-performing configuration. As machine le…
Hyperparameter OptimizationUnlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence
Trilevel learning (TLL) found diverse applications in numerous machine learning applications, ranging from robust hyperparameter optimization to domain adaptation. However, existing researches primarily focus on scenario…
Domain AdaptationHyperparameter OptimizationInnovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach
This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regress…
Hyperparameter OptimizationregressionSentiment AnalysisStock Market PredictionMachine learning approach for mapping the stable orbits around planets
Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML…
Hyperparameter OptimizationHyperparameter Tuning Through Pessimistic Bilevel Optimization
Automated hyperparameter search in machine learning, especially for deep learning models, is typically formulated as a bilevel optimization problem, with hyperparameter values determined by the upper level and the model …
Bilevel OptimizationHyperparameter OptimizationResource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems
On High-Performance Computing (HPC) systems, several hyperparameter configurations can be evaluated in parallel to speed up the Hyperparameter Optimization (HPO) process. State-of-the-art HPO methods follow a bandit-base…
Hyperparameter OptimizationScheduling