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XDeepMVA (Explainable-Deep-Multi-View-Analysis)

####################################################

This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.

####################################################

How to use

####################################################

The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.

###########

Contact

###########

In case of questions, suggestions, problems etc. please send an email.

Tanuj Hasija: tanuj.hasija@sst.upb.de

Maurice Kuschel: maurice.kuschel@sst.upb.de

##############

References

##############

[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.

[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.

[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.

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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
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####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
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####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
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Explainable Deep Multi-View-Analysis
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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
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####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
Skip to content
BranchesTags
Repository files navigation
####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
About
Explainable Deep Multi-View-Analysis
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, 'i');
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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
Skip to content
BranchesTags
Repository files navigation
####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
About
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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
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####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
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GitHub - SSTGroup/XDeepMVA: Explainable Deep Multi-View-Analysis · GitHub
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Repository files navigation
####################################################
XDeepMVA (Explainable-Deep-Multi-View-Analysis)
####################################################
This repository is concerned with providing python implementation for both novel and known algorithms for explainable multiview analysis using deep neural networks.
####################################################
How to use
####################################################
The developer notebook can be used to train the different models implemented. Currently, the data is just randomly generated to allow for exemplary demonstration on how to train and evaluate the models.
###########
Contact
###########
In case of questions, suggestions, problems etc. please send an email.
Tanuj Hasija:
tanuj.hasija@sst.upb.de
Maurice Kuschel:
maurice.kuschel@sst.upb.de
##############
References
##############
[1] S.Vieluf*, T.Hasija*, M.Kuschel, C.Reinsberger, and T.Loddenkemper, "Developing a deep canonical correlation-based technique for seizure prediction", Submitted, 2022.
[2] G.Andrew, R.Arora, J.Bilmes,and K.Livescu, "Deep canonical correlation analysis", International conference on machine learning, 2013.
[3] W.Wang, R.Arora, K.Livescu, and J.Bilmes. "On deep multi-view representation learning." International conference on machine learning, 2015.
About
Explainable Deep Multi-View-Analysis
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