Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models
Large-scale industrial recommendation models predict the most relevant items from catalogs containing millions or billions of options. To train these models efficiently, a small set of irrelevant items (negative samples)…
Hyperparameter OptimizationSequential RecommendationQ-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency o…
Hyperparameter OptimizationQuantum Machine LearningReplacing Paths with Connection-Biased Attention for Knowledge Graph Completion
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test …
Hyperparameter OptimizationKnowledge Graph CompletionLink PredictionRelation PredictionAutomated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significan…
Hyperparameter OptimizationARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tuning such hyperparameters is both costly …
AutoMLBenchmarkingHyperparameter OptimizationReinforcement Learning (RL)A Survey on Neural Architecture Search Based on Reinforcement Learning
The automation of feature extraction of machine learning has been successfully realized by the explosive development of deep learning. However, the structures and hyperparameters of deep neural network architectures also…
Hyperparameter OptimizationNeural Architecture Searchreinforcement-learningReinforcement Learning+1Archon: An Architecture Search Framework for Inference-Time Techniques
Inference-time techniques are emerging as highly effective tools to enhance large language model (LLM) capabilities. However, best practices for developing systems that combine these techniques remain underdeveloped due …
Hyperparameter OptimizationInstruction FollowingLarge Language ModelMathInvestigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance
In the AutoML domain, test accuracy is heralded as the quintessential metric for evaluating model efficacy, underpinning a wide array of applications from neural architecture search to hyperparameter optimization. Howeve…
AutoMLBenchmarkingHyperparameter OptimizationNeural Architecture SearchOnline Nonconvex Bilevel Optimization with Bregman Divergences
Bilevel optimization methods are increasingly relevant within machine learning, especially for tasks such as hyperparameter optimization and meta-learning. Compared to the offline setting, online bilevel optimization (OB…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningLearning Rate Optimization for Deep Neural Networks Using Lipschitz Bandits
Learning rate is a crucial parameter in training of neural networks. A properly tuned learning rate leads to faster training and higher test accuracy. In this paper, we propose a Lipschitz bandit-driven approach for tuni…
Hyperparameter OptimizationCross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks
In this paper, we present a cross-entropy optimization method for hyperparameter optimization in stochastic gradient-based approaches to train deep neural networks. The value of a hyperparameter of a learning algorithm o…
Hyperparameter OptimizationStochastic OptimizationMoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement
Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. Moisture content is a critical attribute …
AttributeHyperparameter OptimizationNeural Architecture SearchTowards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of thes…
AutoMLBayesian OptimizationFeature Engineeringfeature selection+3Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database
Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among…
Feature Importancefeature selectionHyperparameter OptimizationMortality PredictionFastBO: Fast HPO and NAS with Adaptive Fidelity Identification
Hyperparameter optimization (HPO) and neural architecture search (NAS) are powerful in attaining state-of-the-art machine learning models, with Bayesian optimization (BO) standing out as a mainstream method. Extending BO…
Bayesian OptimizationHyperparameter OptimizationNeural Architecture SearchNovel ConceptsStructuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations
Advancing Machine Learning (ML)-based perception models for autonomous systems necessitates addressing weak spots within the models, particularly in challenging Operational Design Domains (ODDs). These are environmental …
Autonomous DrivingHyperparameter Optimizationobject-detectionObject Detection+1A Comparative Study of Hyperparameter Tuning Methods
The study emphasizes the challenge of finding the optimal trade-off between bias and variance, especially as hyperparameter optimization increases in complexity. Through empirical analysis, three hyperparameter tuning al…
Hyperparameter OptimizationregressionAutomated Machine Learning in Insurance
Machine Learning (ML) has gained popularity in actuarial research and insurance industrial applications. However, the performance of most ML tasks heavily depends on data preprocessing, model selection, and hyperparamete…
AutoMLHyperparameter OptimizationModel SelectionA Web-Based Solution for Federated Learning with LLM-Based Automation
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and …
CPUFederated LearningHyperparameter OptimizationLanguage Modelling+3Flexora: Flexible Low Rank Adaptation for Large Language Models
Large Language Models (LLMs) are driving advancements in artificial intelligence by increasing the scale of model parameters, which has significantly enhanced generalization ability and unlocked new capabilities in pract…
Hyperparameter Optimization