Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Gravix: Active Learning for Gravitational Waves Classification Algorithms
This project explores the integration of Bayesian Optimization (BO) algorithms into a base machine learning model, specifically Convolutional Neural Networks (CNNs), for classifying gravitational waves among background n…
Active LearningBayesian OptimizationClassificationHyperparameter OptimizationTowards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
The widespread use of the internet has led to an overwhelming amount of data, which has resulted in the problem of information overload. Recommender systems have emerged as a solution to this problem by providing persona…
Hyperparameter OptimizationRecommendation SystemsExploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (GP) inference by projecting the GP onto M basis functions. These proper…
Hyperparameter OptimizationLMEMs for post-hoc analysis of HPO Benchmarking
The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algo…
BenchmarkingHyperparameter OptimizationAn investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensi…
Evolutionary AlgorithmsHyperparameter OptimizationThe Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines
The recent surge of open-source large language models (LLMs) enables developers to create AI-based solutions while maintaining control over aspects such as privacy and compliance, thereby providing governance and ownersh…
GPUHyperparameter OptimizationLanguage ModelingLanguage Modelling+1AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models
Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unimodal scenarios and require extensive man…
AutoMLCode GenerationFeature EngineeringHyperparameter OptimizationBe aware of overfitting by hyperparameter optimization!
Hyperparameter optimization is very frequently employed in machine learning. However, an optimization of a large space of parameters could result in overfitting of models. In recent studies on solubility prediction the a…
Hyperparameter OptimizationRepresentation LearningQuantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization
The increasing adoption of Artificial Intelligence (AI) in engineering problems calls for the development of calibration methods capable of offering robust statistical reliability guarantees. The calibration of black box…
Hyperparameter OptimizationSchedulingLearning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates
Tuning parameters of a non-differentiable or black-box compute is challenging. Existing methods rely mostly on random sampling or grid sampling from the parameter space. Further, with all the current methods, it is not p…
DenoisingHyperparameter OptimizationImage DenoisingSSIMHyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
This paper focuses on hyperparameter optimization for autonomous driving strategies based on Reinforcement Learning. We provide a detailed description of training the RL agent in a simulation environment. Subsequently, w…
Autonomous DrivingBayesian Optimizationglobal-optimizationHyperparameter Optimization+2Crafting Efficient Fine-Tuning Strategies for Large Language Models
This paper addresses the challenges of efficiently fine-tuning large language models (LLMs) by exploring data efficiency and hyperparameter optimization. We investigate the minimum data required for effective fine-tuning…
AttributeAttribute ExtractionHyperparameter OptimizationA Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction
Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP learns from molecular string representations (e.g., Simplified Molecular Input L…
Drug DiscoveryHyperparameter OptimizationImpacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
In the context of cybersecurity of modern communications networks, Intrusion Detection Systems (IDS) have been continuously improved, many of them incorporating machine learning (ML) techniques to identify threats. Altho…
Hyperparameter OptimizationIntrusion DetectionHO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size sch…
Adversarial RobustnessHyperparameter OptimizationAutomated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to au…
Bayesian OptimizationHyperparameter OptimizationBrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning Algorithms
Objective: Brain metastases (BMs) are common in cancer patients and determining the primary tumor site is crucial for effective treatment. This study aims to predict the primary tumor site from BM MRI data using radiomic…
DiagnosticFeature Importancefeature selectionHyperparameter OptimizationVariational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors and cancer…
Drug Response PredictionHyperparameter OptimizationSmell and Emotion: Recognising emotions in smell-related artworks
Emotions and smell are underrepresented in digital art history. In this exploratory work, we show that recognising emotions from smell-related artworks is technically feasible but has room for improvement. Using style tr…
Hyperparameter OptimizationStyle TransferTerrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data
Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can hav…
Bayesian OptimizationClassificationHyperparameter OptimizationTime Series+1