[HTML][HTML] Capsule networks–a survey
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
Capsule networks for image classification: A review
Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …
transition from human-engineered features to automated features to address challenging …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
Capsule networks–A survey
M Kwabena Patrick, A Felix Adekoya, A Abra Mighty… - 2022 - dl.acm.org
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook
Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
Securing deep spiking neural networks against adversarial attacks through inherent structural parameters
R El-Allami, A Marchisio, M Shafique… - … Design, Automation & …, 2021 - ieeexplore.ieee.org
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
Special session: Towards an agile design methodology for efficient, reliable, and secure ML systems
The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …
However, the current computing infrastructure is insufficient to support all real-world …
Is spiking secure? a comparative study on the security vulnerabilities of spiking and deep neural networks
A Marchisio, G Nanfa, F Khalid… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological
plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs) …
plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs) …