Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
Next revision Both sides next revision
bib:viper_conf [2023/04/07 17:56]
marchand
bib:viper_conf [2024/04/24 21:26]
marchand
Line 1: Line 1:
 +    @inproceedings{ruiz:​sisap2022,​
 +  author ​   = {Bini, Lorenzo and Mojarrad, Fatemeh Nassajian and Liarou, Margarita and Matthes, Thomas and Marchand-Maillet,​ Stéphane},
 +  title     = {{FlowCyt}: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking},​
 +  booktitle = {Conference on Health, Inference, and Learning (CHIL'​24},​
 +  year={2024},​
 +  url    = {https://​arxiv.org/​abs/​2403.00024},​
 +}
 +
     @inproceedings{ruiz:​sisap2022,​     @inproceedings{ruiz:​sisap2022,​
   author ​   = {Ubaldo Ruiz and   author ​   = {Ubaldo Ruiz and
Line 38: Line 46:
 year="​2022",​ year="​2022",​
 publisher="​Springer International Publishing",​ publisher="​Springer International Publishing",​
-address="​Cham",+address="​Bologna, Italy",
 pages="​164--177",​ pages="​164--177",​
 abstract="​Given a finite dataset in a metric space, we investigate the definition of a representative sample. Such a definition is important in data analysis strategies to seed algorithms (such as {\$}{\$}k{\$}{\$}-means) and for pivot-based data indexing techniques. We discuss the geometrical and statistical facets of such a definition.",​ abstract="​Given a finite dataset in a metric space, we investigate the definition of a representative sample. Such a definition is important in data analysis strategies to seed algorithms (such as {\$}{\$}k{\$}{\$}-means) and for pivot-based data indexing techniques. We discuss the geometrical and statistical facets of such a definition.",​
 isbn="​978-3-031-17849-8",​ isbn="​978-3-031-17849-8",​
-note{Best paper award}+note{(Best paper award)}
 } }
  
bib/viper_conf.txt · Last modified: 2024/04/24 22:32 by marchand
--

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion