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Hyperparameter Optimization

Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

Papers

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Showing 101–150 of 813 papers

TitleStatusHype
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Automating Data Science Pipelines with Tensor CompletionCode0
Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency—0
Replacing Paths with Connection-Biased Attention for Knowledge Graph CompletionCode0
Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models—0
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
Online Nonconvex Bilevel Optimization with Bregman Divergences—0
Learning Rate Optimization for Deep Neural Networks Using Lipschitz Bandits—0
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks—0
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement—0
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations—0
A Comparative Study of Hyperparameter Tuning Methods—0
Automated Machine Learning in InsuranceCode1
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
Flexora: Flexible Low Rank Adaptation for Large Language Models—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback—0
LMEMs for post-hoc analysis of HPO BenchmarkingCode0
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary AlgorithmsCode0
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices—0
The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines—0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Be aware of overfitting by hyperparameter optimization!—0
Quantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization—0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning—0
Crafting Efficient Fine-Tuning Strategies for Large Language Models—0
A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction—0
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems—0
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization—0
BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning AlgorithmsCode0
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses—0
Smell and Emotion: Recognising emotions in smell-related artworksCode0
Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data—0
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Scalable Nested Optimization for Deep Learning—0
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random FeaturesCode2
Improving Hyperparameter Optimization with Checkpointed Model WeightsCode1
Fast Optimizer BenchmarkCode1
Enhancing supply chain security with automated machine learning—0
Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning—0
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting—0
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control—0
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