Difference between revisions of "Spring 2017 CS292F Syllabus"
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*04/13 Word embeddings | *04/13 Word embeddings | ||
** [https://people.cs.umass.edu/~arvind/emnlp2014.pdf Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space, Neelakantan et al., EMNLP 2014] | ** [https://people.cs.umass.edu/~arvind/emnlp2014.pdf Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space, Neelakantan et al., 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] | + | ** Keqian Li: [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.aclweb.org/anthology/P15-1173 AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015] | ** [http://www.aclweb.org/anthology/P15-1173 AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015] | ||
*04/18 Neural network basics (Project proposal due, HW1 out) | *04/18 Neural network basics (Project proposal due, HW1 out) | ||
| − | ** [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986] | + | ** Arturo Deza: [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986] |
| − | ** [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016] | + | ** Shayan Sadigh: [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016] |
*04/20 Recursive Neural Networks | *04/20 Recursive Neural Networks | ||
** [https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf Parsing with Compositional Vector Grammars, Socher et al., ACL 2013] | ** [https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf Parsing with Compositional Vector Grammars, Socher et al., ACL 2013] | ||
** [https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Socher et al., EMNLP 2013] | ** [https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf 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) | *04/25 RNNs (NLP seminar: Stanford NLP's Jiwei Li 04/26) | ||
| − | ** [http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf Recurrent neural network based language model] | + | ** Adam Ibrahim: [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] | + | ** Yuanshun Yao: [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] | + | ** Omid Askarisichani: [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] | ||
| − | ** [https://arxiv.org/pdf/1502.02367v3.pdf Gated Feedback Recurrent Neural Networks, Chung et al., ICML 2015] | + | ** Daniel Spokoyny: [https://arxiv.org/pdf/1502.02367v3.pdf Gated Feedback Recurrent Neural Networks, Chung et al., ICML 2015] |
*05/02 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out) | *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] | + | ** Wenhan Xiong: [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] | + | ** Xiaoyong Jin: [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] |
*05/04 Attention mechanisms | *05/04 Attention mechanisms | ||
| − | ** [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015] | + | ** Xinyi Zhang: [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015] |
| − | ** [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015] | + | ** Hanwen Zha: [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015] |
** [http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf End-to-end memory networks, NIPS 2015] | ** [http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf End-to-end memory networks, NIPS 2015] | ||
*05/09 Project: mid-term presentation (1) | *05/09 Project: mid-term presentation (1) | ||
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** [https://arxiv.org/pdf/1510.03820.pdf A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification, Zhang and Wallace, Arxiv 2015] | ** [https://arxiv.org/pdf/1510.03820.pdf A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification, Zhang and Wallace, Arxiv 2015] | ||
*05/18 Language and vision | *05/18 Language and vision | ||
| − | ** [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015] | + | ** Shiliang Tang: [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] | + | ** Aditya Jonnalagadda: [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] |
| − | ** [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, Zhu et al., ICCV 2015] | + | ** Appannacharya Kalyan Tej Javvadi: [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, Zhu et al., ICCV 2015] |
*05/23 Deep Reinforcement Learning 1 | *05/23 Deep Reinforcement Learning 1 | ||
| − | ** [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016] | + | ** Rohan Jain: [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016] |
| − | ** [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016] | + | ** Mahnaz Koupaee: [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016] |
*05/25 Deep Reinforcement Learning 2 | *05/25 Deep Reinforcement Learning 2 | ||
| − | ** [https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013] | + | ** Xin Wang: [https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013] |
** [https://arxiv.org/pdf/1509.02971.pdf Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016] | ** [https://arxiv.org/pdf/1509.02971.pdf Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016] | ||
*05/30 Unsupervised Learning | *05/30 Unsupervised Learning | ||
** [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014] | ** [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014] | ||
| − | ** [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] | + | ** Utkarsh Gaur: [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) | *06/01 Project: final presentation (1) | ||
*06/06 Project: final presentation (2) | *06/06 Project: final presentation (2) | ||
*06/08 Project: final presentation (3) | *06/08 Project: final presentation (3) | ||
*06/10 23:59PM PT Project Final Report Due. | *06/10 23:59PM PT Project Final Report Due. | ||
Revision as of 14:50, 6 April 2017
- 04/04 Introduction, logistics, NLP, and deep learning.
- 04/06 Tips for a successful class project
- 04/11 NLP Tasks
- 04/13 Word embeddings
- Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space, Neelakantan et al., EMNLP 2014
- Keqian Li: Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014
- AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015
- 04/18 Neural network basics (Project proposal due, HW1 out)
- 04/20 Recursive Neural Networks
- 04/25 RNNs (NLP seminar: Stanford NLP's Jiwei Li 04/26)
- 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
- Shiliang Tang: Show and Tell: A Neural Image Caption Generator, CVPR 2015
- Aditya Jonnalagadda: Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy and Li Fei-Fei, CVPR 2015
- Appannacharya Kalyan Tej Javvadi: Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, Zhu et al., ICCV 2015
- 05/23 Deep Reinforcement Learning 1
- 05/25 Deep Reinforcement Learning 2
- 05/30 Unsupervised Learning
- 06/01 Project: final presentation (1)
- 06/06 Project: final presentation (2)
- 06/08 Project: final presentation (3)
- 06/10 23:59PM PT Project Final Report Due.
