Machine Learning Methods in Visualisation for Big Data 2020
Permanent URI for this collection
Norrköping, Sweden, May 25-29, 2020 (Virtual)
Papers
Progressive Multidimensional Projections: A Process Model based on Vector Quantization
[full
paper ] [meta data
]
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
[full
paper ] [meta data
]
Visual Analysis of the Impact of Neural Network Hyper-Parameters
[full
paper ] [meta data
]
Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density
Plots
[full
paper ] [meta data
]
Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders
[full
paper ] [meta data
]
BibTeX (Machine Learning Methods in Visualisation for Big Data 2020)
@inproceedings{10.2312:mlvis.20201099,booktitle = {Machine Learning Methods in Visualisation for Big Data},editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko}, title = {{Progressive Multidimensional Projections: A Process Model based on Vector Quantization}},author = {Ventocilla, Elio AlejandroandMartins, Rafael M.andPaulovich, Fernando V.andRiveiro, Maria}, year = {2020},publisher = {The Eurographics Association},ISBN = {978-3-03868-113-7},DOI = {10.2312/mlvis.20201099}}
@inproceedings{10.2312:mlvis.20201100,booktitle = {Machine Learning Methods in Visualisation for Big Data},editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko}, title = {{ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods}},author = {Schlegel, UdoandCakmak, ErenandKeim, Daniel A.}, year = {2020},publisher = {The Eurographics Association},ISBN = {978-3-03868-113-7},DOI = {10.2312/mlvis.20201100}}
@inproceedings{10.2312:mlvis.20201102,booktitle = {Machine Learning Methods in Visualisation for Big Data},editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko}, title = {{Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots}},author = {Thrun, Michael C.}, year = {2020},publisher = {The Eurographics Association},ISBN = {978-3-03868-113-7},DOI = {10.2312/mlvis.20201102}}
@inproceedings{10.2312:mlvis.20201101,booktitle = {Machine Learning Methods in Visualisation for Big Data},editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko}, title = {{Visual Analysis of the Impact of Neural Network Hyper-Parameters}},author = {Jönsson, DanielandEilertsen, GabrielandShi, HeziandZheng, JianminandYnnerman, AndersandUnger, Jonas}, year = {2020},publisher = {The Eurographics Association},ISBN = {978-3-03868-113-7},DOI = {10.2312/mlvis.20201101}}
@inproceedings{10.2312:mlvis.20201103,booktitle = {Machine Learning Methods in Visualisation for Big Data},editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko}, title = {{Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders}},author = {Grósz, TamásandKurimo, Mikko}, year = {2020},publisher = {The Eurographics Association},ISBN = {978-3-03868-113-7},DOI = {10.2312/mlvis.20201103}}