6 citations · 21 across the 11 of their papers we have counts for
15 papers
Should Machine Learning Models Report to Us When They Are Clueless?
Roozbeh Yousefzadeh, Xuenan Cao
The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolati…
Over-parameterization: A Necessary Condition for Models that Extrapolate
Roozbeh Yousefzadeh
In this work, we study over-parameterization as a necessary condition for having the ability for the models to extrapolate outside the convex hull of training set. We specifically,…
Deep Learning Generalization, Extrapolation, and Over-parameterization
Roozbeh Yousefzadeh
We study the generalization of over-parameterized deep networks (for image classification) in relation to the convex hull of their training sets. Despite their great success, gener…
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
Roozbeh Yousefzadeh
The success of deep neural networks in image classification and learning can be partly attributed to the features they extract from images. It is often speculated about the propert…
To what extent should we trust AI models when they extrapolate?
Roozbeh Yousefzadeh, Xuenan Cao
Many applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually co…
Federated Learning without Revealing the Decision Boundaries
Roozbeh Yousefzadeh
We consider the recent privacy preserving methods that train the models not on original images, but on mixed images that look like noise and hard to trace back to the original imag…