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This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking.
We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation.
Constant-modulus sequence set with low peak side-lobe level is a necessity for enhancing the performance of modern active sensing systems like Multiple Input Multiple Output (MIMO) RADARs. In this paper, we consider the problem of designing a constant-modulus sequence set by minimizing the peak side-lobe level, which can be cast as a non-convex minimax problem, and propose a Majorization-Minimization technique based iterative monotonic algorithm named as the PSL minimizer.
************ PhD position at Inria (Nancy - Grand Est), France **************
(More information: https://jobs.inria.fr/public/classic/en/offres/2021-03399)
Title: Robust and Generalizable Deep Learning-based Audio-visual Speech Enhancement
March 19, 2021
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April 28, 2021
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March 20-21, 2021
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June 16-19, 2021
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June 10, 2021
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April 1-September 30, 2021
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April 19-30, 2021
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July 31-August 7, 2021
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September 1-6 or 15-21, 2021
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September 20-24, 2021
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June 24-28, 2021
Registration Deadline: May 30, 2021