Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Arpteg, Anders"'
Publikováno v:
British Machine Vision Conference (BMVC) 2021
We propose layer saturation - a simple, online-computable method for analyzing the information processing in neural networks. First, we show that a layer's output can be restricted to the eigenspace of its variance matrix without performance loss. We
Externí odkaz:
http://arxiv.org/abs/2006.08679
Several methods have been developed to assess the perceptual quality of audio under transforms like lossy compression. However, they require paired reference signals of the unaltered content, limiting their use in applications where references are un
Externí odkaz:
http://arxiv.org/abs/2006.06287
We propose a metric, Layer Saturation, defined as the proportion of the number of eigenvalues needed to explain 99% of the variance of the latent representations, for analyzing the learned representations of neural network layers. Saturation is based
Externí odkaz:
http://arxiv.org/abs/1907.08589
Surprisingly promising results have been achieved by deep learning (DL) systems in recent years. Many of these achievements have been reached in academic settings, or by large technology companies with highly skilled research groups and advanced supp
Externí odkaz:
http://arxiv.org/abs/1810.12034
Publikováno v:
In The Journal of Systems & Software September 2022 191
Autor:
Arpteg, Anders
Publikováno v:
Sammanfattning på engelska (spikblad).
Diss. Linköping : Univ., 2005.
Externí odkaz:
http://www.bibl.liu.se/liupubl/disp/disp2005/tek946s.pdf
Autor:
Arpteg, Anders
The number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to construct user-driven information extraction systems where novice users are able to adapt them to new domains and t
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5688