A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Filters
MisConv: Convolutional Neural Networks for Missing Data
[article]
2021
arXiv
pre-print
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous ...
By modeling the distribution of missing values by the Mixture of Factor Analyzers, we cover the spectrum of possible replacements and find an analytical formula for the expected value of convolution operator ...
For the purpose of Open Access, the authors have applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ...
arXiv:2110.14010v2
fatcat:5l6kll3qevhyzdi4oklrnkfjmu
Cross-relation based blind identification of acoustic SIMO systems and applications
2017
The increased use of devices controlled by distant speech therefore induces the need for dereverberation. ...
From a more practical perspective, the use of room impulses estimated at a poor accuracy is investigated for the problem of speaker diarization. The spatial information c [...] ...
However, according to results found in the literature of neural networks, the NMCFLMS is guaranteed to converge in noisy conditions only when a non-summable step-size decreasing to 0 is employed, which ...
doi:10.25560/52430
fatcat:ie3nbr3lqnbltjhfgeu5arku6y