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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 126–150 of 813 papers

TitleStatusHype
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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