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| − | *01/16 Introduction, logistics, NLP, and deep learning. | + | *01/4 Introduction, logistics, and deep learning. |
| − | *01/18 Tips for a successful class project | + | *01/6 Tips for a successful class project |
| − | *01/23 NLP Tasks | + | *01/11 |
| − | *01/25 Word embeddings | + | *01/13 Word embeddings |
| − | **Conner : [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]
| + | *01/18 University Holiday: Martin Luther King Jr. Day |
| − | **Sanjana : [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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| − | **Wenhu : [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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| − | *01/30 Neural network basics (Project proposal due to Grader: Ke Ni < ke00@ucsb.edu> , HW1 out) | |
| − | **Jashanvir : [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986]
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| − | **Metehan : [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016]
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| − | **Vivek P.: [http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al., JMLR 2014]
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| − | *02/01 Recursive Neural Networks
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| − | **April : [http://www.robotics.stanford.edu/~ang/papers/emnlp12-SemanticCompositionalityRecursiveMatrixVectorSpaces.pdf Semantic Compositionality through Recursive Matrix-Vector Spaces, Socher et al., EMNLP 2012]
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| − | **Zhiyu : [https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf Parsing with Compositional Vector Grammars, Socher et al., ACL 2013]
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| − | **Andy : [https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Socher et al., EMNLP 2013]
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| − | *02/06 RNNs
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| − | **Lukas : [https://pdfs.semanticscholar.org/8adb/8257a423f55b1f20ba62c8b20118d76a25c7.pdf A Learning Algorithm for Continually Running Fully Recurrent Neural Networks, Ronald J. Williams and David Zipser, 1989]
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| − | **Yifu : [http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf Recurrent neural network based language model]
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| − | **John : [https://arxiv.org/pdf/1308.0850.pdf Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv]
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| − | *02/08 LSTMs/GRUs
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| − | **Liu : [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997]
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| − | **Nidhi : [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014]
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| − | **Vivek A.: [https://arxiv.org/pdf/1502.02367v3.pdf Gated Feedback Recurrent Neural Networks, Chung et al., ICML 2015]
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| − | *02/13 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
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| − | **Ryan : [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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| − | **Yanju : [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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| − | **Karthik : [http://www.aclweb.org/anthology/P16-1100 Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models, Luong and Manning, ACL 2016]
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| − | *02/15 Attention mechanisms
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| − | **Jing : [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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| − | **Abhay : [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015]
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| − | **Ashwini : [http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf End-to-end memory networks, NIPS 2015]
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| − | *02/20 Convolutional Neural Networks (Mid-term report due to Grader: Ke Ni <ke00@ucsb.edu>)
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| − | **Esther : [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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| − | **Maohua : [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]
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| − | **Jiawei : [http://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences Convolutional Neural Network Architectures for Matching Natural Language Sentences, Hu et al., NIPS 2014]
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| − | *02/22 Language and vision
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| − | **Sai : [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015]
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| − | **Xiyou : [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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| − | **Richika : [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]
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| − | *02/27 Deep Reinforcement Learning 1 (HW2 due: 02/26 Monday 11:59pm)
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| − | **Sharon : [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016]
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| − | **David : [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016]
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| − | **Michael : [http://www.aclweb.org/anthology/P16-1153 Deep Reinforcement Learning with a Natural Language Action Space, He et al., ACL 2016]
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| − | *03/01 Deep Reinforcement Learning 2
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| − | **Trevor : [https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013]
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| − | **Calvin : [https://arxiv.org/pdf/1509.02971.pdf Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016]
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| − | **Chani : [https://www.nature.com/articles/nature16961 Mastering the game of Go with deep neural networks and tree search (2016), D. Silver et al., Nature]
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| − | *03/06 Unsupervised Learning
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| − | **Hongmin : [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf Generative Adversarial Nets, Goodfellow et al., NIPS 2014]
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| − | **Burak : [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014]
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| − | **Pushkar : [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]
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| − | **Liu : [http://papers.nips.cc/paper/5949-semi-supervised-sequence-learning.pdf Semi-supervised Sequence Learning, Dai et al., NIPS 2015]
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| − | *03/08 Project: final presentation (1)
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| − | *03/13 Project: final presentation (2)
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| − | *03/15 Project: final presentation (3)
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| | + | Neural network basics (Project proposal due to Grader: Ke Ni < ke00@ucsb.edu> , HW1 out) |
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| | + | *01/20 Recursive Neural Networks |
| | + | *01/25 RNNs |
| | + | *01/27 LSTMs/GRUs |
| | + | *02/1 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out) |
| | + | *02/3 Attention mechanisms |
| | + | *02/8 Convolutional Neural Networks (Mid-term report due to Grader: Ke Ni <ke00@ucsb.edu>) |
| | + | *02/10 Language and vision |
| | + | *02/15 University Holiday: Presidents' Day |
| | + | Deep Reinforcement Learning 1 (HW2 due: 02/26 Monday 11:59pm) |
| | + | *02/17 Deep Reinforcement Learning 2 |
| | + | *02/22 Unsupervised Learning |
| | + | *03/24 Project: final presentation (1) |
| | + | *03/1 Project: final presentation (2) |
| | + | *03/3 Project: final presentation (3) |
| | + | *03/8 Project: final presentation (3) |
| | + | *03/10 Project: final presentation (3) |
| | *03/19 23:59PM PT Project Final Report Due. | | *03/19 23:59PM PT Project Final Report Due. |
Neural network basics (Project proposal due to Grader: Ke Ni < ke00@ucsb.edu> , HW1 out)
