no code implementations • 23 Mar 2022 • Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci
Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development in artificial intelligence, aiming to formally define probabilistic models, guiding development of objective functions, and regularization of probabilistic models.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 16 Nov 2020 • Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Yaser P. Fallah, Rui Guo, HongSheng Lu
In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS).
no code implementations • 22 Oct 2020 • Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Yaser P. Fallah
In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance.
no code implementations • 19 Feb 2020 • Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Yaser P. Fallah, Rui Guo, HongSheng Lu
The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles.
no code implementations • 21 Oct 2019 • Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci, Hassan Foroosh
Instead, the regularizing effects of assuming prior over parameters is seen through maximizing probabilities of models or according to information theory, minimizing the information content of a model.
no code implementations • 26 Sep 2018 • Amir Emad Marvasti, Ehsan Emad Marvasti, George Atia, Hassan Foroosh
We propose a new way of thinking about deep neural networks, in which the linear and non-linear components of the network are naturally derived and justified in terms of principles in probability theory.