8 citations · 12 across the 6 of their papers we have counts for
6 papers
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
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 model…
Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti +3
The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication net…
Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti +1
Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety…
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti +3
The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally…
Maximum Probability Theorem: A Framework for Probabilistic Learning
Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci +1
We present a theoretical framework of probabilistic learning derived by Maximum Probability (MP) Theorem shown in the current paper. In this probabilistic framework, a model is def…
Rediscovering Deep Neural Networks Through Finite-State Distributions
Amir Emad Marvasti, Ehsan Emad Marvasti, George Atia +1
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…