Latest Articles for Parameters

A study on efficiently spacing points in two-dimensional space.

A new method allows machine learning models to forget data effectively and swiftly.

Compact word representations improve language model performance and efficiency.

A look into how linked twist maps show ergodic behavior and shear parameters.

This study develops a model to simulate COVID-19 spread and assess social restrictions.

An insight into how chaotic behavior arises from saddle-foci in various systems.

A new method connects prior knowledge to finite mixture models for clustering.

A new statistical model improving data analysis across various fields.

Utilizing simulations and machine learning to enhance our understanding of star formation.

A new approach to understanding and predicting smoking habits.

Learn how sparsity improves efficiency in multi-task learning models.

A fresh method aims to help models learn new tasks without forgetting old ones.

This article examines parameter uncertainties in thermodynamic models and their effects on predictions.

A new method streamlines climate model tuning, enhancing reliability and saving time.

A new approach to optimization using Riemannian geometry for better results.

A data-driven approach to enhance microgel production efficiency and customization.

Delta-LoRA streamlines fine-tuning for large language models with better performance and less resource use.

Neural ODEs merge neural networks with dynamic systems for advanced learning.

Discover how directed evolution optimizes protein functions for real-world applications.

SparseSwin offers efficient image classification with fewer parameters and high accuracy.

phi-1.5 shows that smaller models can perform well with quality data.

New methods improve efficiency and performance in neural networks using Mixture of Experts.

This article discusses Salient Channel Tuning, a method for efficient fine-tuning of large models.

A new method to enhance predictions in Bayesian deep learning by focusing on key parameters.

A method to improve graph transformers efficiently and with less resource use.

Research sheds light on the formation and stability of spiral arms in galaxies.

Research enhances deep learning models for resource-constrained environments.

Researchers develop a model to study spatial relationships and impacts over distance.

Point Cloud Networks reduce parameters while maintaining performance in deep learning tasks.

This study investigates how bacteria move based on environmental factors using experimental data.

A new approach to predict mortality and enhance disaster response.

Big Jacobi polynomials have unique properties essential for various mathematical applications.

A look at spatio-temporal models in data analysis.

Researchers study a rare particle's lineshape using high-energy collision data.

New model improves analysis of complex relationships in statistics and machine learning.

Introducing a new method to reduce memory use when finetuning large models.

A focus on refining parameter estimation enhances tumor growth model accuracy.

This study highlights the importance of variability in cognitive processes using advanced modeling techniques.

Researchers develop methods to analyze stellar data for better understanding of massive stars.

A look into Parametric Timed Games and their applications in real-time systems.