2 citations · 6 across the 16 of their papers we have counts for
17 papers
SSP-QST: Spectral Subspace Purification for Photonic Quantum State Tomography
Anuvab Sen, Saibal Mukhopadhyay
Photonic quantum sensing often uses low-rank entangled probes such as Greenberger-Horne-Zeilinger (GHZ), Bell, and NOON states. Although these probes are ideally rank-1, practical…
SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms
Minah Lee, Saibal Mukhopadhyay
Swarming systems, such as drone fleets and robotic teams, exhibit complex dynamics driven by both individual behaviors and emergent group-level interactions. Unlike traditional mul…
AIRE-Prune: Asymptotic Impulse-Response Energy for State Pruning in State Space Models
Apurba Prasad Padhy, Fernando Camacho, Saibal Mukhopadhyay
State space models (SSMs) often sacrifice capacity, search space, or stability to offset the memory and compute costs of large state dimensions. We introduce a structured post-trai…
Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences
Mellon M. Zhang, Glen Chou, Saibal Mukhopadhyay
Accurate and low-latency 3D object detection is essential for autonomous driving, where safety hinges on both rapid response and reliable perception. While rotating LiDAR sensors a…
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…
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting
Hemant Kumawat, Saibal Mukhopadhyay
Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approac…
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…