Difference between revisions of "Spring 2017 CS292F Syllabus"

Revision as of 00:14, 4 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 Recursive Neural Networks
- Parsing with Compositional Vector Grammars, Socher et al., ACL 2013
- Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Socher et al., EMNLP 2013
04/25 RNNs (NLP seminar: Stanford NLP's Jiwei Li 04/26)
- Recurrent neural network based language model
- Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv
04/27 LSTMs/GRUs
- Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997
- On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014
05/02 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014
- Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014
05/04 Attention mechanisms
- NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015
- Teaching Machines to Read and Comprehend, NIPS 2015
05/09 Project: mid-term presentation (1)
05/11 Project: mid-term presentation (2)
05/16 Convolutional Neural Networks (HW2 due)
- Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011
- Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014
05/18 Language and vision
- Show and Tell: A Neural Image Caption Generator, CVPR 2015
- Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy and Li Fei-Fei, CVPR 2015
05/23 Deep Reinforcement Learning 1
- Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016
- Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016
05/25 Deep Reinforcement Learning 2
- Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013
- Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016
05/30 Unsupervised Learning
- Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Redford et al., 2015
06/01 Project: final presentation (1)
06/06 Project: final presentation (2)
06/08 Project: final presentation (3)

@@ Line 33: / Line 33: @@
 ** [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015]
 ** [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy and Li Fei-Fei, CVPR 2015]
-*05/23 Speech recognition and understanding
+*05/23 Deep Reinforcement Learning 1
-** [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/HintonDengYuEtAl-SPM2012.pdf Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Hinton et al., 2012 IEEE Signal Proc. Magazine]
+** [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016]
-** [https://www.cs.toronto.edu/~gdahl/papers/DBN4LVCSR-TransASLP.pdf Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition, Dahl et al., 2012 IEEE TASLP]
+** [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016]
-*05/25 Information Extraction
+*05/25 Deep Reinforcement Learning 2
-**[http://www.aclweb.org/anthology/N16-1030 Lample, Guillaume, et al. "Neural Architectures for Named Entity Recognition." Proceedings of NAACL-HLT. 2016.]
+** [https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013]
-**[https://www.aclweb.org/anthology/P/P16/P16-1105.pdf Miwa, Makoto, and Mohit Bansal. "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.]
+** [https://arxiv.org/pdf/1509.02971.pdf Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016]
-*05/30 Summarization
+*05/30 Unsupervised Learning
-** [https://arxiv.org/pdf/1509.00685.pdf A neural attention model for abstractive sentence summarization, Rush et al., EMNLP 2015]
+** [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014]
-** [http://www.ccs.neu.edu/home/luwang/papers/NAACL2016.pdf Neural Network-Based Abstract Generation for Opinions and Arguments, Lu Wang and Wang Ling, NAACL 2016]
+** [https://arxiv.org/pdf/1511.06434.pdf%C3%AF%C2%BC%E2%80%B0 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Redford et al., 2015]
 *06/01 Project: final presentation (1)
 *06/06 Project: final presentation (2)
 *06/08 Project: final presentation (3)

Difference between revisions of "Spring 2017 CS292F Syllabus"

Revision as of 00:14, 4 April 2017

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