Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?
Optimal hyperparameter selection is critical for maximizing neural network performance, especially as models grow in complexity. This work investigates the viability of leveraging large language models (LLMs) for hyperpa…
Computational EfficiencyHyperparameter OptimizationAn Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System
In stochastic multi-armed bandits, a major problem the learner faces is the trade-off between exploration and exploitation. Recently, exploration-free methods -- methods that commit to the action predicted to return the …
Hyperparameter OptimizationMulti-Armed BanditsTerraTorch: The Geospatial Foundation Models Toolkit
TerraTorch is a fine-tuning and benchmarking toolkit for Geospatial Foundation Models built on PyTorch Lightning and tailored for satellite, weather, and climate data. It integrates domain-specific data modules, pre-defi…
BenchmarkingDecoderEarth ObservationHyperparameter OptimizationPSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation
Medical image segmentation, particularly for brain tumor analysis, demands precise and computationally efficient models due to the complexity of multimodal MRI datasets and diverse tumor morphologies. This study introduc…
Brain Tumor SegmentationHyperparameter OptimizationImage SegmentationMedical Image Segmentation+2HyperNOs: Automated and Parallel Library for Neural Operators Research
This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive explor…
Hyperparameter OptimizationA nonlinear real time capable motion cueing algorithm based on deep reinforcement learning
In motion simulation, motion cueing algorithms are used for the trajectory planning of the motion simulator platform, where workspace limitations prevent direct reproduction of reference trajectories. Strategies such as …
Deep Reinforcement LearningHyperparameter OptimizationModel Predictive ControlTrajectory PlanningHyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings
This paper explores the application of bandit algorithms in both stochastic and adversarial settings, with a focus on theoretical analysis and practical applications. The study begins by introducing bandit problems, dist…
Bayesian OptimizationComputational EfficiencyEfficient ExplorationHyperparameter OptimizationThe Role of Hyperparameters in Predictive Multiplicity
This paper investigates the critical role of hyperparameters in predictive multiplicity, where different machine learning models trained on the same dataset yield divergent predictions for identical inputs. These inconsi…
FairnessHyperparameter OptimizationPredictionIntegration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models
The variability and biases in the real-world performance benchmarking of deep learning models for medical imaging compromise their trustworthiness for real-world deployment. The common approach of holding out a single fi…
BenchmarkingHyperparameter OptimizationDiscriminative versus Generative Approaches to Simulation-based Inference
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to da…
Hyperparameter Optimizationparameter estimationClustering-based Meta Bayesian Optimization with Theoretical Guarantee
Bayesian Optimization (BO) is a well-established method for addressing black-box optimization problems. In many real-world scenarios, optimization often involves multiple functions, emphasizing the importance of leveragi…
Bayesian OptimizationClusteringHyperparameter OptimizationMeta-LearningULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
Hyperparameter optimization (HPO) is a billion-dollar problem in machine learning, which significantly impacts the training efficiency and model performance. However, achieving efficient and robust HPO in deep reinforcem…
Bayesian OptimizationDeep Reinforcement LearningHyperparameter OptimizationReinforcement Learning (RL)MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System
E-commerce search optimization has evolved to include a wider range of metrics that reflect user engagement and business objectives. Modern search frameworks now incorporate advanced quality features, such as sales count…
Bayesian OptimizationHyperparameter OptimizationRetrievalPredictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining
The impressive capabilities of Large Language Models (LLMs) across diverse tasks are now well-established, yet their effective deployment necessitates careful hyperparameter optimization. Through extensive empirical stud…
GPUHyperparameter OptimizationLanguage ModelingLanguage Modelling+2AutoQML: A Framework for Automated Quantum Machine Learning
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervent…
AutoMLHyperparameter OptimizationQuantum Machine LearningAutoML for Multi-Class Anomaly Compensation of Sensor Drift
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performanc…
AutoMLEnsemble Learningfeature selectionHyperparameter Optimization+1Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods
Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs can have serious consequences. Current UQ…
Hyperparameter OptimizationUncertainty QuantificationFaster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems
While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters and hyperparameters that must be made or…
Bayesian OptimizationHyperparameter OptimizationLanguage ModelingLanguage Modelling+3MetaDE: Evolving Differential Evolution by Differential Evolution
As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE …
Computational EfficiencyGPUHyperparameter OptimizationApplication-oriented automatic hyperparameter optimization for spiking neural network prototyping
Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established machine learning (ML) solutions to the d…
Hyperparameter Optimization