Difference between revisions of "Winter 2018 CS291A Syllabus"

From courses
Jump to: navigation, search
(update student choices)
 
(18 intermediate revisions by 2 users not shown)
Line 3: Line 3:
 
*01/23 NLP Tasks
 
*01/23 NLP Tasks
 
*01/25 Word embeddings  
 
*01/25 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]
+
**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]
−
** Arya: [http://www.anthology.aclweb.org/D/D14/D14-1162.pdf Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014]
+
**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]
−
** : [http://www.aclweb.org/anthology/P15-1173 AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015]
+
**Wenhu : [http://www.aclweb.org/anthology/P15-1173 AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes, Rothe and Schutze, ACL 2015]
 
*01/30 Neural network basics (Project proposal due to Grader: Ke Ni < ke00@ucsb.edu> , HW1 out)
 
*01/30 Neural network basics (Project proposal due to Grader: Ke Ni < ke00@ucsb.edu> , HW1 out)
−
** : [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986]
+
**Jashanvir : [http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Learning representations by back-propagating errors, Nature, 1986]
−
**Dan : [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016]
+
**Metehan : [https://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016]
−
**Vivek : [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]
+
**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]
 
*02/01 Recursive Neural Networks  
 
*02/01 Recursive Neural Networks  
 
**April : [http://www.robotics.stanford.edu/~ang/papers/emnlp12-SemanticCompositionalityRecursiveMatrixVectorSpaces.pdf Semantic Compositionality through Recursive Matrix-Vector Spaces, Socher et al., EMNLP 2012]
 
**April : [http://www.robotics.stanford.edu/~ang/papers/emnlp12-SemanticCompositionalityRecursiveMatrixVectorSpaces.pdf Semantic Compositionality through Recursive Matrix-Vector Spaces, Socher et al., EMNLP 2012]
−
** : [https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf Parsing with Compositional Vector Grammars, Socher et al., ACL 2013]
+
**Zhiyu : [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]
+
**Andy : [https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Socher et al., EMNLP 2013]
 
*02/06 RNNs
 
*02/06 RNNs
 
**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]
 
**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]
Line 20: Line 20:
 
*02/08 LSTMs/GRUs
 
*02/08 LSTMs/GRUs
 
**Liu : [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997]
 
**Liu : [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997]
−
**Zhiyu : [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014]
+
**Nidhi : [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]
+
**Vivek A.: [https://arxiv.org/pdf/1502.02367v3.pdf Gated Feedback Recurrent Neural Networks, Chung et al., ICML 2015]
 
*02/13 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
 
*02/13 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
 
**Ryan : [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014]
 
**Ryan : [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014]
 
**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]
 
**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]
−
** : [http://www.aclweb.org/anthology/P16-1100 Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models, Luong and Manning, ACL 2016]
+
**Karthik : [http://www.aclweb.org/anthology/P16-1100 Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models, Luong and Manning, ACL 2016]
 
*02/15 Attention mechanisms
 
*02/15 Attention mechanisms
 
**Jing : [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015]
 
**Jing : [https://arxiv.org/pdf/1409.0473.pdf NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015]
Line 32: Line 32:
 
*02/20 Convolutional Neural Networks  (Mid-term report due to Grader: Ke Ni <ke00@ucsb.edu>)
 
*02/20 Convolutional Neural Networks  (Mid-term report due to Grader: Ke Ni <ke00@ucsb.edu>)
 
**Esther : [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011]
 
**Esther : [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011]
−
**Shabnam : [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]
+
**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]
 
**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]
 
**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]
 
*02/22 Language and vision
 
*02/22 Language and vision
Line 38: Line 38:
 
**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]
 
**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]
 
**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]
 
**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]
−
*02/27 Deep Reinforcement Learning 1 (HW2 due)
+
*02/27 Deep Reinforcement Learning 1 (HW2 due: 02/26 Monday 11:59pm)
−
**Karthik : [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016]
+
**Sharon : [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016]
 
**David : [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016]
 
**David : [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016]
 
**Michael : [http://www.aclweb.org/anthology/P16-1153 Deep Reinforcement Learning with a Natural Language Action Space, He et al., ACL 2016]
 
**Michael : [http://www.aclweb.org/anthology/P16-1153 Deep Reinforcement Learning with a Natural Language Action Space, He et al., ACL 2016]
Line 48: Line 48:
 
*03/06 Unsupervised Learning
 
*03/06 Unsupervised Learning
 
**Hongmin : [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf Generative Adversarial Nets, Goodfellow et al., NIPS 2014]
 
**Hongmin : [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf Generative Adversarial Nets, Goodfellow et al., NIPS 2014]
−
** : [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014]
+
**Burak : [https://arxiv.org/abs/1312.6114 Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014]
 
**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]
 
**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]
 +
**Liu : [http://papers.nips.cc/paper/5949-semi-supervised-sequence-learning.pdf Semi-supervised Sequence Learning, Dai et al., NIPS 2015]
 
*03/08 Project: final presentation (1)  
 
*03/08 Project: final presentation (1)  
 +
**Andy Chen
 +
**Ashwini Patil, Sai Nikhil Maram
 +
**David Bernadett
 +
**Ishani Gupta, Nidhi Hiremath
 +
**Wenhu Chen, Zhiyu Chen
 
*03/13 Project: final presentation (2)
 
*03/13 Project: final presentation (2)
 +
**Ismet Burak Kadron
 +
**Jiawei Wu, Jing Qian
 +
**XiyouZhou, JiangyueCai
 +
**Maohua Zhu, Liu Liu
 +
**Pushkar Shukla, Richika Sharan
 +
**Sanjana Sahayaraj, Vivek Adarsh
 +
**Esther, Lukas
 
*03/15 Project: final presentation (3)
 
*03/15 Project: final presentation (3)
 +
**Sharon Levy
 +
**Conner Vercellino, Calvin Wang
 +
**Trevor Morris, Chani Jindal
 +
**Vivek Pradhan, Abhay Chennagiri
 +
**Jashanvir Singh Taggar, Metehan Cekic
 +
**Yanju Chen, Hongmin Wang
 +
 
*03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@ucsb.edu>
 
*03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@ucsb.edu>

Latest revision as of 11:43, 6 March 2018

  • 03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@ucsb.edu>
Retrieved from "http://william.cs.ucsb.edu/courses/index.php?title=Winter_2018_CS291A_Syllabus&oldid=464"