4 citations · 10 across the 9 of their papers we have counts for
5 papers · 1 filter
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
Biswadeep Chakraborty, Harshit Kumar, Saibal Mukhopadhyay
Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases, leading to homogenized node representations and a loss of expressiveness. In…
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
Beomseok Kang, Harshit Kumar, Minah Lee +2
Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively…
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
Harshit Kumar, Sudarshan Sharma, Biswadeep Chakraborty +1
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance L…
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
Biswadeep Chakraborty, Beomseok Kang, Harshit Kumar +1
Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses…
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
Harshit Kumar, Beomseok Kang, Biswadeep Chakraborty +1
This paper presents the first systematic study of evaluating Deep Neural Networks (DNNs) designed to forecast the evolution of stochastic complex systems. We show that traditional…