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Updates to the Deep Learning Bibliography from Li Dong (and Knut O. Hellan

We’re of course grateful for updates with new bibliography entries to the Deep Learning Bibliography from researchers and practitioners in the field (note: we don’t mind if you suggest your own papers to the bibliography as long as they are within deep learning).

The update with new bibliography entries today is suggested by Li Dong (State Key Lab of Software Development Environment, Beihang University in Beijing, China) and Knut O. Hellan (Zedge, Trondheim, Norway).

How to contribute to the Deep Learning bibliography?

The Deep Learning bibliography can be found on github:

1. How to contribute – with git / github?

  1. Fork the repository (e.g. with upper right button on github)
  2. Add new entries or updates to entries in the file: bibtex/deeplearninggpuwithkeywords2014.bib
  3. Do a pull request about changes back to this repository from your forked one

See also https://help.github.com/articles/using-pull-requests

2. How to contribute – with email?

Send an email to amund@memkite.com with bibtex entries for the publications.

Updates can be found separately below, and with all the other entries the Deeplearning.University Bibliography

Best regards,

Amund Tveit (@atveit)
Memkite Deep Learning Engineering Team

New updates in the repository this time

  title={Learning Sparse Feature Representations for Music Annotation and Retrievals},
  author={Nam, Juhan and Herrera, Jorge and Slaney, Malcolm and Smith, Julius},

	title = "Unsupervised Feature Learning for Audio Classification using Convolutional Deep Belief Networks",
	author = "Honglak Lee and Yan Largman and Peter Pham and Andrew~Y. Ng",
	booktitle = "Advances in Neural Information Processing Systems 22",
	pages = "1096--1104",
	year = "2009"

  title={Unsupervised learning of hierarchical representations with convolutional deep belief networks},
  author={Honglak Lee and Roger Grosse and Rajesh Ranganath and A.~Y. Ng},
  journal={Communications of the ACM},

  title={Multimodal Deep Learning},
  author={Ngiam, J. and Khosla, A. and Kim, M. and Nam, J. and Lee, H. and Ng, A.~Y.},

	title = {Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical representations},
	author = {Lee, Honglak and Grosse, Roger and Ranganath, Rajesh and Ng, Andrew Y.},
        booktitle = {Proceedings of the 26th International Conference on Machine Learning},
	year = {2009},
	pages = {609--616},

	author = {Sohn, Kihyuk and Jung, Dae Yon and Lee, Honglak and Hero III, Alfred},
	title = {Efficient Learning of Sparse, Distributed, Convolutional Feature Representations for Object Recognition},
	booktitle = {Proceedings of 13th International Conference on Computer Vision},
	year = {2011}

  title={Learning hierarchical representations for face verification with convolutional deep belief networks},
  author={Huang, G.~B. and Lee, H. and Learned-Miller, E.},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  title={Deep learning for detecting robotic grasps},
  author={Lenz, Ian and Lee, Honglak and Saxena, Ashutosh},
  booktitle={Robotics: Science and Systems},

  title={Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines},
  author={Mittelman, Roni and Lee, Honglak and Kuipers, Benjamin and Savarese, Silvio},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  title={Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling},
  author={Kae, Andrew and Sohn, Kihyuk and Lee, Honglak and Learned-Miller, Erik},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  title={Learning and Selecting Features Jointly with Point-wise Gated {Boltzmann} Machines},
  author={Sohn, Kihyuk and Zhou, Guanyu and Lee, Chansoo and Lee, Honglak},
  booktitle={Proceedings of The 30th International Conference on Machine Learning},

  title={Structured Recurrent Temporal Restricted Boltzmann Machines},
  author={Roni Mittelman and Benjamin Kuipers and Silvio Savarese and Honglak Lee},
  booktitle={Proceedings of The 31st International Conference on Machine Learning},

	title = {Sparse Deep Belief Net Model for Visual Area {V2}},
	author = {Honglak Lee and Chaitanya Ekanadham and Andrew~Y. Ng},
	booktitle = {Advances in Neural Information Processing Systems 20},
	pages = {873--880},
	year = {2008}

  keywords = {Sentiment Analysis,Natural Language Processing},
  title={Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis},
  author={Dong, Li and Wei, Furu and Zhou, Ming and Xu, Ke},
  booktitle={Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI)},

  keywords = {Sentiment Analysis,Natural Language Processing},
  title={Adaptive recursive neural network for target-dependent twitter sentiment classification},
  author={Dong, Li and Wei, Furu and Tan, Chuanqi and Tang, Duyu and Zhou, Ming and Xu, Ke},
  booktitle={Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL)},

About Amund Tveit (@atveit - amund@memkite.com)

Amund Tveit works in Memkite on developing large-scale Deep Learning and Search (Convolutional Neural Network) with Swift and Metal for iOS (see deeplearning.education for a Memkite app video demo). He also maintains the deeplearning.university bibliography (github.com/memkite/DeepLearningBibliography)

Amund previously co-founded Atbrox , a cloud computing/big data service company (partner with Amazon Web Services), also doing some “sweat equity” startup investments in US and Nordic startups. His presentations about Hadoop/Mapreduce Algorithms and Search were among top 3% of all SlideShare presentations in 2013 and his blog posts has been frequently quoted by Big Data Industry Leaders and featured on front pages of YCombinator News and Reddit Programming

He previously worked for Google, where he was tech.lead for Google News for iPhone (mentioned as “Google News Now Looks Beautiful On Your iPhone” on Mashable.com), lead a team measuring and improving Google Services in the Scandinavian Countries (Maps and Search) and worked as a software engineer on infrastructure projects. Other work experience include telecom (IBM Canada) and insurance/finance (Storebrand).

Amund has a PhD in Computer Science. His publications has been cited more than 500 times. He also holds 4 US patents in the areas of search and advertisement technology, and a pending US patent in the area of brain-controlled search with consumer-level EEG devices.

Amund enjoys coding, in particular Python, C++ and Swift (iOS)