no code implementations • 16 Oct 2023 • 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.
2 code implementations • 4 Jun 2023 • Anubhab Ghosh, Antoine Honoré, Saikat Chatterjee
DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements of the state.
1 code implementation • 1 Jul 2021 • Anubhab Ghosh, Antoine Honoré, Dong Liu, Gustav Eje Henter, Saikat Chatterjee
For a standard speech phone classification setup involving 39 phones (classes) and the TIMIT dataset, we show that the use of standard features called mel-frequency-cepstral-coeffcients (MFCCs), the proposed generative models, and the decision fusion together can achieve $86. 6\%$ accuracy by generative training only.
no code implementations • 15 Feb 2021 • Anubhab Ghosh, Antoine Honoré, Dong Liu, Gustav Eje Henter, Saikat Chatterjee
We test the robustness of a maximum-likelihood (ML) based classifier where sequential data as observation is corrupted by noise.
1 code implementation • 13 Oct 2019 • Dong Liu, Antoine Honoré, Saikat Chatterjee, Lars K. Rasmussen
In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation.
no code implementations • 17 Nov 2017 • Antoine Honoré, Veronica Siljehav, Saikat Chatterjee, Eric Herlenius
Even with a limited and unbalanced training data, the large neural network provides a detection performance level that is feasible to use in clinical care.