Difference between revisions of "Spring 2017 CS292F Syllabus"

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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
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*04/11 Word embeddings  
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*04/11 NLP Tasks
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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]
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*04/13 Word embeddings  
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** [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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** Christian Bueno: [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]
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** [http://www.anthology.aclweb.org/D/D14/D14-1162.pdf Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014]
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** 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]
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*04/13 Knowledge base embeddings
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** Mengya Tao: [http://www.aclweb.org/anthology/P15-1173 AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015]
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** [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, HW1 out)
 
*04/18 Neural network basics (Project proposal due, HW1 out)
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*04/20 Neural networks language models
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** Arturo Deza: [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986]
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** [http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf A Neural Probabilistic Language Model, Bengio, JMLR 2003]
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** Shayan Sadigh: [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016]
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*04/25 RNNs  
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*04/20 Recursive Neural Networks
 +
** Yun Zhao: [https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf Parsing with Compositional Vector Grammars, Socher et al., ACL 2013]
 +
** Rachel Redberg: [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)
 
** [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]  
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** [https://arxiv.org/pdf/1308.0850.pdf Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv]
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** 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]
 
** [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]
 +
** 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)
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** [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014]
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** 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]
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** [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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** 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
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** [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015]
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** Xinyi Zhang:  [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015]
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** [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015]
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** Hanwen Zha: [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015]
 +
** Zhujun Xiao: [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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** JONNALAGADDA, ADITYA
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** ZHA, HANWEN
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** AGHAKHANI, HOJJAT
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** JAIN, ROHAN
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** WANG, XIN
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** KOUPAEE, MAHNAZ
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** YAO, YUANSHUN
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** LI, ZHIJING
 
*05/11 Project: mid-term presentation (2)
 
*05/11 Project: mid-term presentation (2)
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** SPOKOYNY, DANIEL
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** ZHANG, FANGJUN
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** FEINN, ZACHARY
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** JIN, XIAOYONG
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** REDBERG, RACHEL
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** XIONG, WENHAN
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** ZHAO, YUN
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** SADIGH, SHAYAN
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** XIAO, ZHUJUN
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** ZHANG, XINYI
 
*05/16 Convolutional Neural Networks  (HW2 due)
 
*05/16 Convolutional Neural Networks  (HW2 due)
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** [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011]
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** Zachary Feinn: [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011]
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** [http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014]
+
** Fangjun Zhang: [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
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** [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015]
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** 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]
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** 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]
−
*05/23 Speech recognition and understanding
+
** : [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]
−
** [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]
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*05/23 Deep Reinforcement Learning 1
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** [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]
+
** Rohan Jain: [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016]
−
*05/25 Information Extraction
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** Mahnaz Koupaee: [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016]
−
*05/30 Summarization
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*05/25 Deep Reinforcement Learning 2
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** [https://arxiv.org/pdf/1509.00685.pdf A neural attention model for abstractive sentence summarization EMNLP 2015]
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** Xin Wang: [https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013]
−
*06/01 Question answering
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** Zhijing Li: [https://arxiv.org/pdf/1509.02971.pdf Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016]
−
*06/06 Project: final presentation (1)
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*05/30 Unsupervised Learning
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*06/08 Project: final presentation (2)
+
** : [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014]
 +
** Hojjat Aghakhani: [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)
 +
**ADITYA
 +
**XINYI
 +
**RACHEL
 +
**YUANSHUN
 +
**HANWEN
 +
**ROHAN
 +
*06/06 Project: final presentation (2)
 +
**MAHNAZ
 +
**YUN
 +
**XIN
 +
**SHAYAN
 +
**ZHIJING
 +
**ZHUJUN
 +
**ZACHARY
 +
*06/08 Project: final presentation (3)
 +
**XIAOYONG
 +
**DANIEL
 +
**WENHAN
 +
**HOJJAT
 +
**SHILIANG
 +
**FANGJUN
 +
*06/10 23:59PM PT Project Final Report Due.

Latest revision as of 15:41, 25 May 2017

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