3 citations · 4 across the 7 of their papers we have counts for
7 papers
Scalable Edge-assisted Fusion and Path Prediction for Connected Autonomous Vehicles
Tyler Landle, Jackson Isenberg, Abhijit Chatterjee +2
The planning algorithms inside an Autonomous Vehicle (AV) rely on information from on-board sensors whose line of sight is limited by emerging traffic conditions and occlusions. Ed…
DANCE: Dynamic 3D CNN Pruning: Joint Frame, Channel, and Feature Adaptation for Energy Efficiency on the Edge
Mohamed Mejri, Ashiqur Rasul, Abhijit Chatterjee
Modern convolutional neural networks (CNNs) are workhorses for video and image processing, but fail to adapt to the computational complexity of input samples in a dynamic manner to…
eCAV: An Edge-Assisted Evaluation Platform for Connected Autonomous Vehicles
Tyler Landle, Jordan Rapp, Dean Blank +4
As autonomous vehicles edge closer to widespread adoption, enhancing road safety through collision avoidance and minimization of collateral damage becomes imperative. Vehicle-to-ev…
RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing
Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
Modern transformer-based encoder-decoder architectures struggle with reasoning tasks due to their inability to effectively extract relational information between input objects (dat…
LARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules
Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
Human cognition excels at symbolic reasoning, deducing abstract rules from limited samples. This has been explained using symbolic and connectionist approaches, inspiring the devel…
A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge
Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
In recent years, both online and offline deep learning models have been developed for time series forecasting. However, offline deep forecasting models fail to adapt effectively to…
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks
Chandramouli Amarnath, Aishwarya H. Balwani, Kwondo Ma +1
Deep neural networks (DNNs) are now the de facto choice for computer vision tasks such as image classification. However, their complexity and "black box" nature often renders the s…