Search Results for author:
Found 4 papers, 1 papers with code
On Continuity of Robust and Accurate Classifiers
To this end, we will introduce a framework for a rigorous study of harmonic and holomorphic hypothesis in learning theory terms and provide empirical evidence that continuous hypotheses does not perform as well as discontinuous hypotheses in some common machine learning tasks.
An Analytic Framework for Robust Training of Artificial Neural Networks
The reliability of a learning model is key to the successful deployment of machine learning in various industries.
Modeling and Eliminating Adversarial Examples using Function Theory of Several Complex Variables
This paper presents a model and a solution for the existence and transfer of adversarial examples in analytic hypotheses.
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks
Also, we extend and unify some of the other proposals in the literature and provide alternative explanations on the observations made in those proposals.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
