36 citations · 49 across the 21 of their papers we have counts for
22 papers
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…
Online Relational Inference for Evolving Multi-agent Interacting Systems
Beomseok Kang, Priyabrata Saha, Sudarshan Sharma +2
We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using stre…
RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator
Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay
Developing agents that can perform complex control tasks from high-dimensional observations is a core ability of autonomous agents that requires underlying robust task control poli…
Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves…
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…
Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature ha…
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…
STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents
Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay
Learning accurate, data-driven predictive models for multiple interacting agents following unknown dynamics is crucial in many real-world physical and social systems. In many scena…
Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction
Biswadeep Chakraborty, Saibal Mukhopadhyay
Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to updat…
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
Beomseok Kang, Biswadeep Chakraborty, Saibal Mukhopadhyay
We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features lea…
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
Biswadeep Chakraborty, Saibal Mukhopadhyay
This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction perfo…
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…
XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection
Harshit Kumar, Biswadeep Chakraborty, Sudarshan Sharma +1
Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, t…
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…
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…
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection
Biswadeep Chakraborty, Xueyuan She, Saibal Mukhopadhyay
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is ba…
Cost-aware Feature Selection for IoT Device Classification
Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat +2
Classification of IoT devices into different types is of paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machin…
Design and Evaluation of Self-Assembled Actin-Based Nano-Communication
Oussama Abderrahmane Dambri, Soumaya Cherkaoui, Biswadeep Chakraborty
The tremendous progress in nanotechnology over the last century, makes it possible to engineer tiny nanodevices, which they need a nano-communication network to interact. Two solut…