[HTML][HTML] Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
Highlights•Artificial and biological neural networks can be analyzed using similar
methods.•Neural analysis has revealed similarities between the representations in artificial …
methods.•Neural analysis has revealed similarities between the representations in artificial …
Low-rank matrix completion by Riemannian optimization
B Vandereycken - SIAM Journal on Optimization, 2013 - SIAM
The matrix completion problem consists of finding or approximating a low-rank matrix based
on a few samples of this matrix. We propose a new algorithm for matrix completion that …
on a few samples of this matrix. We propose a new algorithm for matrix completion that …
Incremental gradient on the grassmannian for online foreground and background separation in subsampled video
It has recently been shown that only a small number of samples from a low-rank matrix are
necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems …
necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems …
Matrix completion for multi-label image classification
Recently, image categorization has been an active research topic due to the urgent need to
retrieve and browse digital images via semantic keywords. This paper formulates image …
retrieve and browse digital images via semantic keywords. This paper formulates image …
Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge
With pervasive sensors continuously collecting and storing massive amounts of information,
there is no doubt this is an era of data deluge. Learning from these large volumes of data is …
there is no doubt this is an era of data deluge. Learning from these large volumes of data is …