no code implementations • 16 Aug 2022 • James Wensel, Hayat Ullah, Arslan Munir
Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing.
no code implementations • 9 Aug 2022 • Hayat Ullah, Arslan Munir
These deep learning algorithms have shown impressive performance for the human activity recognition task.
no code implementations • 9 Nov 2021 • Mahmood Azhar Qureshi, Arslan Munir
We also generate a two-dimensional (2D) mesh architecture of Phantom neural computational cores, which we refer to as Phantom-2D accelerator, and propose a novel dataflow that supports all layers of a CNN, including unit and non-unit stride convolutions, and FC layers.
no code implementations • 19 Jul 2020 • Mahmood Azhar Qureshi, Arslan Munir
The designed core provides a 200% increase in peak throughput per PE count while only incurring a 6% increase in area overhead compared to a single, linear multiplier PE core with same output bit precision.
no code implementations • 14 Nov 2018 • Vahid Behzadan, Roman V. Yampolskiy, Arslan Munir
This paper presents a novel approach to the technical analysis of wireheading in intelligent agents.
1 code implementation • 14 Nov 2018 • Vahid Behzadan, James Minton, Arslan Munir
This paper presents TrolleyMod v1. 0, an open-source platform based on the CARLA simulator for the collection of ethical decision-making data for autonomous vehicles.
no code implementations • 23 Oct 2018 • Vahid Behzadan, Arslan Munir
Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm.
no code implementations • 4 Jun 2018 • Vahid Behzadan, Arslan Munir
Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial examples.
no code implementations • 4 Jun 2018 • Vahid Behzadan, Arslan Munir
With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments.
no code implementations • 23 May 2018 • Vahid Behzadan, Arslan Munir, Roman V. Yampolskiy
The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety.
4 code implementations • 23 Dec 2017 • Vahid Behzadan, Arslan Munir
Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations.
no code implementations • 18 Sep 2017 • Avishek Bose, Arslan Munir, Neda Shabani
In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases.
1 code implementation • 16 Jan 2017 • Vahid Behzadan, Arslan Munir
Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples.