LinkedIn26d
How can you avoid model generalization in machine learning?
Model generalization is the ability of a machine learning model to perform well on new and unseen data that is different from the data it was trained on. It is a desirable goal for any machine ...
Nature6d
Neural Networks and Generalization Error in Machine Learning
Neural networks have become a cornerstone of modern machine learning, enabling significant advancements in various applications, from image recognition to natural language processing. A critical ...
A new geometric machine learning method promises to accelerate precision drug development
Proteins play a crucial role in nearly all biological processes, yet predicting their complex interactions and designing proteins with new functions poses a significant challenge. In a new study ...
IEEE1mon
Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning
Abstract: Federated learning (FL) introduces new perspectives in machine learning (ML ... and most important model generalization without compromising privacy. Zero-Shot Learning (ZSL) and synthetic ...
GitHub4y
Meta Dropout: Learning to Perturb Latent Features for Generalization
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test examples, we may ...
GitHub1y
Domain Generalization by Distribution Estimation
This repository provides the Pytorch code for the work "Domain Generalization by Distribution Estimation" published in International Journal of Machine Learning and Cybernetics, 2023. We briefly ...
LinkedIn29d
How can you avoid model generalization in machine learning?
Learn how to preprocess your data, select and optimize your model, and evaluate and interpret your model, to achieve better model generalization in machine learning.