Difference between revisions of "Spring 2017 CS292F Syllabus"
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(Created page with "*04/04 Introduction, logistics, NLP, and deep learning. *04/06 Tips for a successful class project *04/11 Word embeddings (HW1 out) ** [https://ronan.collobert.com/pub/matos/...") |
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** [https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008] | ** [https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008] | ||
** [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013] | ** [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013] | ||
| − | ** [http://www.anthology.aclweb.org/D/D14/D14-1162.pdf Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014 ] | + | ** [http://www.anthology.aclweb.org/D/D14/D14-1162.pdf Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014] |
*04/13 Knowledge base embeddings | *04/13 Knowledge base embeddings | ||
| + | ** [http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Nickel_438.pdf A three-way model for collective learning on multi-relational data, M Nickel, V Tresp, HP Kriegel, ICML 2011] | ||
| + | ** [http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf Translating embeddings for modeling multi-relational data, A Bordes, N Usunier, A Garcia-Duran, NIPS 2013] | ||
| + | ** [https://arxiv.org/pdf/1503.00759.pdf A Review of Relational Machine Learning for Knowledge Graphs, Nichel et al., Proceedings of the IEEE] | ||
*04/18 Neural network basics (Project proposal due) | *04/18 Neural network basics (Project proposal due) | ||
*04/20 Neural networks language models | *04/20 Neural networks language models | ||
Revision as of 20:29, 25 March 2017
- 04/04 Introduction, logistics, NLP, and deep learning.
- 04/06 Tips for a successful class project
- 04/11 Word embeddings (HW1 out)
- A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008
- Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013
- Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014
- 04/13 Knowledge base embeddings
- A three-way model for collective learning on multi-relational data, M Nickel, V Tresp, HP Kriegel, ICML 2011
- Translating embeddings for modeling multi-relational data, A Bordes, N Usunier, A Garcia-Duran, NIPS 2013
- A Review of Relational Machine Learning for Knowledge Graphs, Nichel et al., Proceedings of the IEEE
- 04/18 Neural network basics (Project proposal due)
- 04/20 Neural networks language models
- 04/25 RNNs (HW1 due and HW2 out)
- 04/27 LSTMs/GRUs
- 05/02 Convolutional Neural Networks (1)
- 05/04 Convolutional Neural Networks (2)
- 05/09 Project: mid-term presentation (1)
- 05/11 Project: mid-term presentation (2) (HW2 due)
- 05/16 Language and vision
- 05/18 Information extraction
- 05/23 Speech recognition and understanding
- 05/25 Sequence-to-sequence models and neural machine translation
- 05/30 Attention mechanisms in NLP
- 06/01 Question answering
- 06/06 Project: final presentation (1)
- 06/08 Project: final presentation (2)
