Difference between revisions of "Spring 2017 CS292F Syllabus"

Revision as of 12:43, 1 May 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
  • Christian Bueno: 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
  • Mengya Tao: 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)
  • Arturo Deza: Learning representations by back-propagating errors, Nature, 1986
  • Shayan Sadigh: An overview of gradient descent optimization algorithms, Sebastian Ruder, Arxiv 2016
• 04/20 Recursive Neural Networks
  • Yun Zhao: Parsing with Compositional Vector Grammars, Socher et al., ACL 2013
  • Rachel Redberg: 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)
  • Recurrent neural network based language model
  • Yuanshun Yao: Generating Sequences With Recurrent Neural Networks, Alex Graves, 2013 arxiv
• 04/27 LSTMs/GRUs
  • Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997
  • On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014
  • Daniel Spokoyny: 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)
  • Wenhan Xiong: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014
  • Xiaoyong Jin: Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014
• 05/04 Attention mechanisms
  • Xinyi Zhang: NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE, Bahdanau et al., ICLR 2015
  • Hanwen Zha: Teaching Machines to Read and Comprehend, NIPS 2015
  • Zhujun Xiao: End-to-end memory networks, NIPS 2015
• 05/09 Project: mid-term presentation (1)
  • TAO, MENGYA
  • JONNALAGADDA, ADITYA
  • ZHA, HANWEN
  • AGHAKHANI, HOJJAT
  • TANG, SHILIANG
  • JAIN, ROHAN
  • WANG, XIN
  • KOUPAEE, MAHNAZ
  • YAO, YUANSHUN
  • BUENO, CHRISTIAN
  • LI, ZHIJING
• 05/11 Project: mid-term presentation (2)
  • SPOKOYNY, DANIEL
  • ZHANG, FANGJUN
  • FEINN, ZACHARY
  • JIN, XIAOYONG
  • REDBERG, RACHEL
  • XIONG, WENHAN
  • ZHAO, YUN
  • LI, CAUCHY LEE
  • SADIGH, SHAYAN
  • XIAO, ZHUJUN
  • ZHANG, XINYI
• 05/16 Convolutional Neural Networks (HW2 due)
  • Zachary Feinn: Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011
  • Fangjun Zhang: A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification, Zhang and Wallace, Arxiv 2015
• 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
  •  : 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
  • Rohan Jain: Deep Reinforcement Learning for Dialogue Generation, Li et al., EMNLP 2016
  • Mahnaz Koupaee: Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Narasimh et al., EMNLP 2016
• 05/25 Deep Reinforcement Learning 2
  • Xin Wang: Playing Atari with Deep Reinforcement Learning, Mnih et al., NIPS workshop 2013
  • Zhijing Li: Continuous control with deep reinforcement learning, Lillicrap et al, ICLR 2016
• 05/30 Unsupervised Learning
  •  : Auto-encoding variational Bayes, Kingma and Welling, ICLR 2014
  • Hojjat Aghakhani: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Redford et al., 2015
• 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.