Difference between revisions of "Spring 2017 CS292F Syllabus"
From courses
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*04/04 Introduction, logistics, NLP, and deep learning. | *04/04 Introduction, logistics, NLP, and deep learning. | ||
*04/06 Tips for a successful class project | *04/06 Tips for a successful class project | ||
| − | *04/11 Word embeddings | + | *04/11 Word embeddings |
** [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] | ||
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** [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] | ** [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] | ** [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, HW1 out) |
*04/20 Neural networks language models | *04/20 Neural networks language models | ||
** [http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf A Neural Probabilistic Language Model, Bengio, JMLR 2003] | ** [http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf A Neural Probabilistic Language Model, Bengio, JMLR 2003] | ||
| − | *04/25 RNNs | + | *04/25 RNNs |
** [http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf Recurrent neural network based language model] | ** [http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf Recurrent neural network based language model] | ||
** [https://arxiv.org/pdf/1308.0850.pdf Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv] | ** [https://arxiv.org/pdf/1308.0850.pdf Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv] | ||
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** [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997] | ** [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997] | ||
** [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014] | ** [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014] | ||
| − | *05/02 Sequence-to-sequence models and neural machine translation | + | *05/02 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out) |
** [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014] | ** [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014] | ||
** [https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014] | ** [https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014] | ||
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** [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015] | ** [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015] | ||
*05/09 Project: mid-term presentation (1) | *05/09 Project: mid-term presentation (1) | ||
| − | *05/11 Project: mid-term presentation (2 | + | *05/11 Project: mid-term presentation (2) |
| − | *05/16 Convolutional Neural Networks | + | *05/16 Convolutional Neural Networks (HW2 due) |
** [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011] | ** [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011] | ||
** [http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014] | ** [http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014] | ||
Revision as of 17:39, 3 April 2017
- 04/04 Introduction, logistics, NLP, and deep learning.
- 04/06 Tips for a successful class project
- 04/11 Word embeddings
- 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, HW1 out)
- 04/20 Neural networks language models
- 04/25 RNNs
- 04/27 LSTMs/GRUs
- 05/02 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
- 05/04 Attention mechanisms
- 05/09 Project: mid-term presentation (1)
- 05/11 Project: mid-term presentation (2)
- 05/16 Convolutional Neural Networks (HW2 due)
- 05/18 Language and vision
- 05/23 Speech recognition and understanding
- 05/25 Information Extraction
- 05/30 Summarization
- 06/01 Question answering
- 06/06 Project: final presentation (1)
- 06/08 Project: final presentation (2)
