[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-10408: -- Priority: Major (was: Minor) Issue Type: Improvement (was: Umbrella) > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Assignee: Alexander Ulanov > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers > References: > 1. Vincent, Pascal, et al. "Extracting and composing robust features with > denoising autoencoders." Proceedings of the 25th international conference on > Machine learning. ACM, 2008. > http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf > > 2. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, > 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. > (2010). Stacked denoising autoencoders: Learning useful representations in a > deep network with a local denoising criterion. Journal of Machine Learning > Research, 11(3371–3408). > http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf > 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep > networks." Advances in neural information processing systems 19 (2007): 153. > http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-10408: -- Target Version/s: (was: 2.0.0) > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Assignee: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers > References: > 1. Vincent, Pascal, et al. "Extracting and composing robust features with > denoising autoencoders." Proceedings of the 25th international conference on > Machine learning. ACM, 2008. > http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf > > 2. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, > 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. > (2010). Stacked denoising autoencoders: Learning useful representations in a > deep network with a local denoising criterion. Journal of Machine Learning > Research, 11(3371–3408). > http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf > 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep > networks." Advances in neural information processing systems 19 (2007): 153. > http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-10408: -- Assignee: Alexander Ulanov Target Version/s: 2.0.0 > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Assignee: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers > References: > 1. Vincent, Pascal, et al. "Extracting and composing robust features with > denoising autoencoders." Proceedings of the 25th international conference on > Machine learning. ACM, 2008. > http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf > > 2. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, > 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. > (2010). Stacked denoising autoencoders: Learning useful representations in a > deep network with a local denoising criterion. Journal of Machine Learning > Research, 11(3371–3408). > http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf > 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep > networks." Advances in neural information processing systems 19 (2007): 153. > http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-10408: - Description: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers References: 1. Vincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." Proceedings of the 25th international conference on Machine learning. ACM, 2008. http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf 2. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(3371–3408). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep networks." Advances in neural information processing systems 19 (2007): 153. http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf was: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers References: 1, 2. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(3371–3408). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep networks." Advances in neural information processing systems 19 (2007): 153. http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers > References: > 1. Vincent, Pascal, et al. "Extracting and composing robust features with > denoising autoencoders." Proceedings of the 25th international conference on > Machine learning. ACM, 2008. > http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf > > 2. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, > 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. > (2010). Stacked denoising autoencoders: Learning useful representations in a > deep network with a local denoising criterion. Journal of Machine Learning > Research, 11(3371–3408). > http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf > 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep > networks." Advances in neural information processing systems 19 (2007): 153. > http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-10408: - Description: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers References: 1, 2. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(3371–3408). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep networks." Advances in neural information processing systems 19 (2007): 153. http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf was: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers: References: 1-3. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf 4. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers > References: > 1, 2. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf, > 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. > (2010). Stacked denoising autoencoders: Learning useful representations in a > deep network with a local denoising criterion. Journal of Machine Learning > Research, 11(3371–3408). > http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484=rep1=pdf > 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep > networks." Advances in neural information processing systems 19 (2007): 153. > http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-10408: - Description: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers: References: 1-3. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf 4. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf was: Goal: Implement various types of autoencoders Requirements: 1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf] 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here 3)Denoising autoencoder 4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers: > References: > 1-3. > http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf > 4. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-10408) Autoencoder
[ https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-10408: - Issue Type: Umbrella (was: Improvement) > Autoencoder > --- > > Key: SPARK-10408 > URL: https://issues.apache.org/jira/browse/SPARK-10408 > Project: Spark > Issue Type: Umbrella > Components: ML >Affects Versions: 1.5.0 >Reporter: Alexander Ulanov >Priority: Minor > > Goal: Implement various types of autoencoders > Requirements: > 1)Basic (deep) autoencoder that supports different types of inputs: binary, > real in [0..1]. real in [-inf, +inf] > 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature > to the MLP and then used here > 3)Denoising autoencoder > 4)Stacked autoencoder for pre-training of deep networks. It should support > arbitrary network layers -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
