Difference between revisions of "Group Reading S17"
From courses
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**Thien: Learning to Skim Text, Yu et al., https://arxiv.org/pdf/1704.06877.pdf | **Thien: Learning to Skim Text, Yu et al., https://arxiv.org/pdf/1704.06877.pdf | ||
**Tate: Convolutional Sequence to Sequence Learning, Gehring et al, https://arxiv.org/abs/1705.03122 | **Tate: Convolutional Sequence to Sequence Learning, Gehring et al, https://arxiv.org/abs/1705.03122 | ||
| + | |||
| + | *05/24 | ||
| + | **A Deep Reinforced Model for Abstractive Summarization, Paulus et al., https://metamind.io/static/pdf/deep-reinforced-model-arxiv-v1.pdf | ||
Revision as of 11:01, 11 May 2017
- 04/12:
- Daniel: Controllable Text Generation, Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing, https://arxiv.org/pdf/1703.00955.pdf
- Mahnaz: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim. https://arxiv.org/pdf/1703.05192.pdf
- 04/19:
- Philip: Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning, Abhishek Das et al. , https://arxiv.org/pdf/1703.06585.pdf
- Rohan: Grammar Variational Autoencoder https://arxiv.org/pdf/1703.01925.pdf
- 04/26
- No meeting. NLP Seminar: Jiwei Li (Stanford) 2pm HFH 1132, and student meeting 4pm in the lab.
- 05/03
- Wenhan Xiong: Reinforcement Learning for Knowledge Graph Reasoning.
- 05/10
- Yun Zhao: Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders, Zhao et al., https://arxiv.org/pdf/1703.10960.pdf ACL 2017
- Shaoyi Zhang: A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues, Serban et al., http://www.cs.toronto.edu/~lcharlin/papers/vhred_aaai17.pdf AAAI 2017
- 05/17
- Thien: Learning to Skim Text, Yu et al., https://arxiv.org/pdf/1704.06877.pdf
- Tate: Convolutional Sequence to Sequence Learning, Gehring et al, https://arxiv.org/abs/1705.03122
- 05/24
- A Deep Reinforced Model for Abstractive Summarization, Paulus et al., https://metamind.io/static/pdf/deep-reinforced-model-arxiv-v1.pdf
