4 papers
Derangetropy Operators
Masoud Ataei, Sepideh Forouzi
A derangetropy operator reweighs a probability density by a fixed profile of its own cumulative distribution function, acting through ranks alone. We prove that these operators are…
Generalized Derangetropy Functionals for Modeling Cyclical Information Flow
Masoud Ataei, Xiaogang Wang
This paper introduces a framework for modeling cyclical and feedback-driven information flow through a generalized family of entropy-modulated transformations called derangetropy f…
Mathematical Programming Models for Exact and Interpretable Formulation of Neural Networks
Masoud Ataei, Edrin Hasaj, Jacob Gipp +1
This paper presents a unified mixed-integer programming framework for training sparse and interpretable neural networks. We develop exact formulations for both fully connected and…
Efficient and Interpretable Neural Networks Using Complex Lehmer Transform
Masoud Ataei, Xiaogang Wang
We propose an efficient and interpretable neural network with a novel activation function called the weighted Lehmer transform. This new activation function enables adaptive featur…