36 citations · 49 across the 21 of their papers we have counts for
5 papers · 1 filter
FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning
Biswadeep Chakraborty, Saibal Mukhopadhyay
We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computa…
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
Biswadeep Chakraborty, Hemant Kumawat, Beomseok Kang +1
Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Gr…
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…
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…
DARTS: Model Uncertainty-Aware Differentiable Architecture Search
Biswadeep Chakraborty, Saibal Mukhopadhyay
We present a Model Uncertainty-aware Differentiable ARchiTecture Search (DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We in…