Latest Articles for Model Performance

Active learning reduces labeling costs while improving model performance.

This article examines data reduction methods in deep learning for better efficiency.

TwinLiteNet offers an efficient solution for semantic segmentation in self-driving cars.

A new approach to mix data for better language model performance.

Introducing a method to enhance learning in machine learning models while preserving important features.

A new dataset aims to improve hate speech detection models for the German language.

Study shows smaller models perform well with simplified training data.

New algorithms improve Maxent model training for predicting wildfire occurrences.

A novel approach to protect image privacy in technology while maintaining model performance.

A novel approach to classifying unseen audio-visual content.

A look into how LLMs summarize code and factors affecting their performance.

A new method combines 3D layouts and text for better urban scene creation.

Research shows data splitting affects performance in language-related tasks.

This study compares methods for assessing AI model performance in healthcare.

A framework for safer data processing in machine learning.

A fresh approach to enhance self-supervised learning using binning in tabular data.

Exploring how Federated Learning can enhance data privacy in healthcare.

A novel approach improves performance and fairness in federated learning.

FedSC improves model training while maintaining user privacy in federated learning.

This article discusses salutary labeling, a method to reduce human input in machine learning.

Including non-English data improves vision-language model performance and cultural understanding.

A method using MCMC for effective negative sample generation in contrastive learning.

Increasing communication rounds reduces costs and improves model performance in federated learning.

A framework to identify and reduce biases in training datasets.

A new method to edit language models effectively while maintaining performance.

A look into the benefits and limitations of dataset distillation in machine learning.

A new benchmark evaluates reasoning skills in language models.

DIPS addresses data quality issues in pseudo-labeling for better machine learning outcomes.

A look at controlling language model behavior with the KL-then-steer technique.

Exploring how attention sinks impact language model performance and introducing a calibration technique.

FedMap improves Federated Learning efficiency while ensuring data privacy.

Insights on the challenges of machine learning in predicting material properties.

Code poisoning enhances risks of membership inference attacks on sensitive data.

A new approach to evaluate ML models focusing on data preparation.

Exploring machine learning models and new datasets for improved security.

A new method for central banks to improve monetary policy choices.

This paper explores ways to better assess model calibration and predictive accuracy.

A new dataset to study label noise in text classification.

A new method enhances detection of mislabeled images and text in datasets.

New methods improve accuracy in medical image segmentation across varied data sources.