46 citations · 56 across the 12 of their papers we have counts for
12 papers
DNS: Data-driven Nonlinear Smoother for Complex Model-free Process
Fredrik Cumlin, Anubhab Ghosh, Saikat Chatterjee
We propose data-driven nonlinear smoother (DNS) to estimate a hidden state sequence of a complex dynamical process from a noisy, linear measurement sequence. The dynamical process…
VSE: Variational state estimation of complex model-free process
Gustav Norén, Anubhab Ghosh, Fredrik Cumlin +1
We design a variational state estimation (VSE) method that provides a closed-form Gaussian posterior of an underlying complex dynamical process from (noisy) nonlinear measurements.…
Chasing Elusive Memory Bugs in GPU Programs
Anubhab Ghosh, Ajay Nayak, Dhananjay Rao Thallikar Shyam +1
Memory safety bugs, such as out-of-bound accesses (OOB) in GPU programs, can compromise the security and reliability of GPU-accelerated software. We report the existence of input-d…
pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
Anubhab Ghosh, Yonina C. Eldar, Saikat Chatterjee
We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements of a process whose underlying state transition…
Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment
Fredrik Cumlin, Xinyu Liang, Anubhab Ghosh +1
Non-intrusive speech quality assessment (SQA) systems suffer from limited training data and costly human annotations, hindering their generalization to real-time conferencing calls…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements
Anubhab Ghosh, Yonina C. Eldar, Saikat Chatterjee
We consider data-driven Bayesian state estimation from compressed measurements (BSCM) of a model-free process. The dimension of the temporal measurement vector is lower than that o…
Compressed Sensing of Generative Sparse-latent (GSL) Signals
Antoine Honoré, Anubhab Ghosh, Saikat Chatterjee
We consider reconstruction of an ambient signal in a compressed sensing (CS) setup where the ambient signal has a neural network based generative model. The generative model has a…
DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup
Anubhab Ghosh, Antoine Honoré, Saikat Chatterjee
We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori…
DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
Anubhab Ghosh, Mohamed Abdalmoaty, Saikat Chatterjee +1
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a…
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
Anubhab Ghosh, Antoine Honoré, Dong Liu +2
In pursuit of explainability, we develop generative models for sequential data. The proposed models provide state-of-the-art classification results and robust performance for speec…
Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows
Anubhab Ghosh, Antoine Honoré, Dong Liu +2
We test the robustness of a maximum-likelihood (ML) based classifier where sequential data as observation is corrupted by noise. The hypothesis is that a generative model, that com…