Search Results for author: Arslan Munir

Found 10 papers, 3 papers with code

NeuroMAX: A High Throughput, Multi-Threaded, Log-Based Accelerator for Convolutional Neural Networks

no code implementations19 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.

Unity

Emergence of Addictive Behaviors in Reinforcement Learning Agents

no code implementations14 Nov 2018 Vahid Behzadan, Roman V. Yampolskiy, Arslan Munir

This paper presents a novel approach to the technical analysis of wireheading in intelligent agents.

Q-Learning

TrolleyMod v1.0: An Open-Source Simulation and Data-Collection Platform for Ethical Decision Making in Autonomous Vehicles

1 code implementation14 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.

Autonomous Vehicles Decision Making

The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learning

no code implementations23 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.

Autonomous Navigation

Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise

no code implementations4 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.

Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles

no code implementations4 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.

Autonomous Navigation Motion Planning

A Psychopathological Approach to Safety Engineering in AI and AGI

no code implementations23 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.

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

4 code implementations23 Dec 2017 Vahid Behzadan, Arslan Munir

Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations.

A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

no code implementations18 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.

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

1 code implementation16 Jan 2017 Vahid Behzadan, Arslan Munir

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples.

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