4 citations · 8 across the 5 of their papers we have counts for
17 papers
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagati…
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems
Burhan A. Mudassar, Sho Ko, Maojingjing Li +2
Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the t…
Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks
Biswadeep Chakraborty, Saibal Mukhopadhyay
A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applica…
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
Harshit Kumar, Nikhil Chawla, Saibal Mukhopadhyay
Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine le…
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data
Priyabrata Saha, Saibal Mukhopadhyay
In this paper, we consider the problem of learning prediction models for spatiotemporal physical processes driven by unknown partial differential equations (PDEs). We propose a dee…
Low Power Unsupervised Anomaly Detection by Non-Parametric Modeling of Sensor Statistics
Ahish Shylendra, Priyesh Shukla, Saibal Mukhopadhyay +2
This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel Density Estimation (KDE)-based…