7 citations · 13 across the 2 of their papers we have counts for
28 papers
Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs
Erik Ostrowski, Muhammad Shafique
When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensi…
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…
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli +3
In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object…
An Empirical Evaluation of LLMs for Solving Offensive Security Challenges
Minghao Shao, Boyuan Chen, Sofija Jancheska +4
Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs…
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defen…
Reduce: A Framework for Reducing the Overheads of Fault-Aware Retraining
Muhammad Abdullah Hanif, Muhammad Shafique
Fault-aware retraining has emerged as a prominent technique for mitigating permanent faults in Deep Neural Network (DNN) hardware accelerators. However, retraining leads to huge ov…