129 citations · 740 across the 61 of their papers we have counts for
10 papers · 1 filter
DECADE: Towards Designing Efficient-yet-Accurate Distance Estimation Modules for Collision Avoidance in Mobile Advanced Driver Assistance Systems
Muhammad Zaeem Shahzad, Muhammad Abdullah Hanif, Muhammad Shafique
The proliferation of smartphones and other mobile devices provides a unique opportunity to make Advanced Driver Assistance Systems (ADAS) accessible to everyone in the form of an a…
SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms
Niraj Pudasaini, Muhammad Abdullah Hanif, Muhammad Shafique
Optimizing Deep Learning-based Simultaneous Localization and Mapping (DL-SLAM) algorithms is essential for efficient implementation on resource-constrained embedded platforms, enab…
Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings
Aya El Mir, Lukelo Thadei Luoga, Boyuan Chen +2
Deploying Multi-Modal Large Language Models (MLLMs) in healthcare is hindered by their high computational demands and significant memory requirements, which are particularly challe…
PENDRAM: Enabling High-Performance and Energy-Efficient Processing of Deep Neural Networks through a Generalized DRAM Data Mapping Policy
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Convolutional Neural Networks (CNNs), a prominent type of Deep Neural Networks (DNNs), have emerged as a state-of-the-art solution for solving machine learning tasks. To improve th…
Robust ADAS: Enhancing Robustness of Machine Learning-based Advanced Driver Assistance Systems for Adverse Weather
Muhammad Zaeem Shahzad, Muhammad Abdullah Hanif, Muhammad Shafique
In the realm of deploying Machine Learning-based Advanced Driver Assistance Systems (ML-ADAS) into real-world scenarios, adverse weather conditions pose a significant challenge. Co…
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition
Nishant Suresh Aswani, Amira Guesmi, Muhammad Abdullah Hanif +1
Continual learning (CL) has spurred the development of several methods aimed at consolidating previous knowledge across sequential learning. Yet, the evaluations of these methods h…
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani +2
Monocular depth estimation (MDE) has advanced significantly, primarily through the integration of convolutional neural networks (CNNs) and more recently, Transformers. However, con…
MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices
Abdul Basit, Khizar Hussain, Muhammad Abdullah Hanif +1
Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained…
Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks
Kamila Zaman, Tasnim Ahmed, Muhammad Kashif +3
In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural networks (HQNNs) represent a promising solution that combines the strengths of classical machin…
A Comparative Analysis of Hybrid-Quantum Classical Neural Networks
Kamila Zaman, Tasnim Ahmed, Muhammad Abdullah Hanif +2
Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current no…