Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Michalis Vlachos"'
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 17:1-22
Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we prop
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Publikováno v:
2020 IEEE International Conference on Data Mining (ICDM)
ICDM
ICDM
A common assumption in embedding methodologies is the availability of exact pairwise distances. In this paper, we propose a 2D embedding that overcomes this limitation. It can operate on distances that are represented as a range of lower and upper bo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a552ac5b40f34ec1b53afe01d37e83a4
https://serval.unil.ch/resource/serval:BIB_8779613E5E2B.P001/REF.pdf
https://serval.unil.ch/resource/serval:BIB_8779613E5E2B.P001/REF.pdf
Autor:
Avi Arampatzis, Evangelos Kanoulas, Theodora Tsikrika, Stefanos Vrochidis, Anastasia Giachanou, Dan Li, Mohammad Aliannejadi, Michalis Vlachos, Guglielmo Faggioli, Nicola Ferro
This volume LNCS 14163 constitutes the refereed proceedings of 14th International Conference of the CLEF Association, CLEF 2023, in Thessaloniki, Greece, during September 18–21, 2023. The 10 full papers and one short paper included in this book wer
Publikováno v:
BigCom
FIDE is a method for embedding high-dimensional datasets on the Euclidean plane that seeks not only to preserve pairwise distances and correlations but also to provide an easily interpretable embedding on two dimensions. To enhance the interpretabili
Autor:
Michalis Vlachos, Johannes Schneider
Publikováno v:
Advances in Intelligent Data Analysis XIX ISBN: 9783030742508
IDA
Advances in Intelligent Data Analysis XIX, pp. 63-75
IDA
Advances in Intelligent Data Analysis XIX, pp. 63-75
We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder to transform the non-interpretable representation of the given layer to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7825ff3f3f937a6143bf2562b28131e0
http://arxiv.org/abs/2005.13630
http://arxiv.org/abs/2005.13630
Autor:
Johannes Schneider, Kathrin Wardatzky, Michalis Vlachos, Francesco Fusco, Vasileios Vasileiadis
Publikováno v:
IJCAI
Neural systems offer high predictive accuracy but are plagued by long training times and low interpretability. We present a simple neural architecture for recommender systems that lifts several of these shortcomings. Firstly, the approach has a high
Autor:
Reinhard Heckel, Michalis Vlachos
Publikováno v:
Proceedings of the 2017 SIAM International Conference on Data Mining
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0a54abe86f3f1561125b93518b6cbd4
https://doi.org/10.1137/1.9781611974973.74
https://doi.org/10.1137/1.9781611974973.74
Publikováno v:
Scopus-Elsevier