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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 251–275 of 813 papers

TitleStatusHype
Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations—0
A Comparative Study of Hyperparameter Tuning Methods—0
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
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
Scalable Nested Optimization for Deep Learning—0
Enhancing supply chain security with automated machine learning—0
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