Difference between revisions of "Spring 2017 CS292F Syllabus"
From courses
Line 13: | Line 13: | ||
*04/25 RNNs (HW1 due and HW2 out) | *04/25 RNNs (HW1 due and HW2 out) | ||
* [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] | ||
*04/27 LSTMs/GRUs | *04/27 LSTMs/GRUs | ||
+ | ** [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] | ||
*05/02 Convolutional Neural Networks (1) | *05/02 Convolutional Neural Networks (1) | ||
** [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] |
Revision as of 21:10, 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)
- Recurrent neural network based language model
- Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv
- 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)