Difference between revisions of "Winter 2018 CS291A Syllabus"
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** : [https://arxiv.org/abs/1506.03340 Teaching Machines to Read and Comprehend, NIPS 2015] | ** : [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] | ||
| − | *02/20 | + | *02/20 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] | ** : [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011] | ||
** : [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] | ||
| − | * | + | *02/22 Language and vision |
** : [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015] | ** : [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] | ** : [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] | ** : [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 |
** : [https://aclweb.org/anthology/D16-1127, Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016] | ** : [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] | ** : [https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016] | ||
| − | *03/ | + | *03/01 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] | ** : [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] | ||
| − | *03/ | + | *03/06 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] | ** : [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] | ||
| − | *03/15 Project: final presentation ( | + | *03/08 Project: final presentation (1) |
| + | *03/13 Project: final presentation (2) | ||
| + | *03/15 Project: final presentation (3) | ||
*03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@umail.ucsb.edu> | *03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@umail.ucsb.edu> | ||
Revision as of 01:26, 16 January 2018
- 01/16 Introduction, logistics, NLP, and deep learning.
- 01/18 Tips for a successful class project
- 01/23 NLP Tasks
- 01/25 Word embeddings
- : Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space, Neelakantan et al., EMNLP 2014
- : 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
- 01/30 Neural network basics (Project proposal due to Grader: Ke Ni <ke00@umail.ucsb.edu> , HW1 out)
- 02/01 Recursive Neural Networks
- 02/06 RNNs
- 02/08 LSTMs/GRUs
- 02/13 Sequence-to-sequence models and neural machine translation (HW1 due and HW2 out)
- 02/15 Attention mechanisms
- 02/20 Convolutional Neural Networks (HW2 due)
- 02/22 Language and vision
- 02/27 Deep Reinforcement Learning 1
- 03/01 Deep Reinforcement Learning 2
- 03/06 Unsupervised Learning
- 03/08 Project: final presentation (1)
- 03/13 Project: final presentation (2)
- 03/15 Project: final presentation (3)
- 03/23 23:59PM PT Project Final Report Due. Grader: Ke Ni <ke00@umail.ucsb.edu>
