Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"
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If you registered this class, you should contact the instructor to lead the discussion of one paper below. | If you registered this class, you should contact the instructor to lead the discussion of one paper below. | ||
If you don't lead the discussion, you will then need to write a 3-page final report in NIPS 2017 style, | If you don't lead the discussion, you will then need to write a 3-page final report in NIPS 2017 style, | ||
| − | comparing any two of the papers below. | + | comparing any two of the papers below. '''Due: 12/18, 23:59pm PT''' to william@cs.ucsb.edu. |
*09/26: | *09/26: | ||
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*11/14: | *11/14: | ||
| + | ** Richard: Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.pdf | ||
| + | ** Ke: Deep reinforcement learning from human preferences, Christiano et al., https://arxiv.org/pdf/1706.03741.pdf | ||
*11/21: | *11/21: | ||
** Conner: Attention Is All You Need, Vaswani et al., 2017 https://arxiv.org/abs/1706.03762 | ** Conner: Attention Is All You Need, Vaswani et al., 2017 https://arxiv.org/abs/1706.03762 | ||
| + | ** Xiaoyong: Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf | ||
*11/28: | *11/28: | ||
| + | ** Austin: Deep Reinforcement Learning that Matters, Henderson et al., arxiv https://arxiv.org/pdf/1709.06560.pdf | ||
| + | ** Adam: Generalization in Deep Learning, https://arxiv.org/pdf/1710.05468.pdf | ||
*12/05: No meeting, NIPS conference. | *12/05: No meeting, NIPS conference. | ||
| Line 53: | Line 58: | ||
===Reinforcement Learning=== | ===Reinforcement Learning=== | ||
* Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf | * Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf | ||
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* Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf | * Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf | ||
* Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf | * Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf | ||
| Line 63: | Line 66: | ||
* Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al., NIPS 2017 https://arxiv.org/abs/1706.00387 | * Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al., NIPS 2017 https://arxiv.org/abs/1706.00387 | ||
* Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf | * Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf | ||
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===Generation=== | ===Generation=== | ||
* Generating Sentences by Editing Prototypes, Guu et al., arxiv https://arxiv.org/abs/1709.08878 | * Generating Sentences by Editing Prototypes, Guu et al., arxiv https://arxiv.org/abs/1709.08878 | ||
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* Adversarially Regularized Autoehttp://www.aclweb.org/anthology/D17-1120ncoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf | * Adversarially Regularized Autoehttp://www.aclweb.org/anthology/D17-1120ncoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf | ||
Latest revision as of 18:00, 28 November 2017
Time: Tuesday 5-6pm. Location: HFH 1132.
If you registered this class, you should contact the instructor to lead the discussion of one paper below. If you don't lead the discussion, you will then need to write a 3-page final report in NIPS 2017 style, comparing any two of the papers below. Due: 12/18, 23:59pm PT to william@cs.ucsb.edu.
- 09/26:
- Mahnaz Summer research presentation: Reinforced Pointer-Generator Network for Abstractive Summarization.
- Xin: FeUdal Networks for Hierarchical Reinforcement Learning, Vezhnevets et al., ICML 2017 https://arxiv.org/pdf/1703.01161.pdf
- 10/03:
- Vivek: The strange geometry of skip-gram with negative sampling, David Mimno and Laure Thompson, http://aclweb.org/anthology/D17-1307
- Yijun: Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf
- 10/10:
- Wenhu: Generating Sentences by Editing Prototypes, Guu et al., arxiv https://arxiv.org/abs/1709.08878
- Ethan: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments, Lowe et al., NIPS 2017 https://arxiv.org/abs/1706.02275
- 10/17:
- Hongmin: An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098
- Xiyou: Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf
- 10/24:
- John: Dict2Vec: Learning Word Embeddings using Dictionaires, Julien Tissier and Christophe Gravier and Amaury Habrard http://aclweb.org/anthology/D17-1024
- Jiawei: Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia and Percy Liang https://arxiv.org/abs/1707.07328
- 10/31:
- Shayan: A simple neural network module for relational reasoning, Santoro et al., Arxiv https://arxiv.org/abs/1706.01427
- Trevor: Multi-Task Video Captioning with Video and Entailment Generation, ACL 2017 Outstanding Paper https://arxiv.org/pdf/1704.07489.pdf
- 11/07:
- Gavin: Hindsight Experience Replay, Andrychowicz et al, https://arxiv.org/pdf/1707.01495.pdf
- Arya: The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf
- 11/14:
- Richard: Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.pdf
- Ke: Deep reinforcement learning from human preferences, Christiano et al., https://arxiv.org/pdf/1706.03741.pdf
- 11/21:
- Conner: Attention Is All You Need, Vaswani et al., 2017 https://arxiv.org/abs/1706.03762
- Xiaoyong: Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf
- 11/28:
- Austin: Deep Reinforcement Learning that Matters, Henderson et al., arxiv https://arxiv.org/pdf/1709.06560.pdf
- Adam: Generalization in Deep Learning, https://arxiv.org/pdf/1710.05468.pdf
- 12/05: No meeting, NIPS conference.
- 12/12: No meeting, NAACL deadline.
Contents
Word Embeddings
- Mimicking Word Embeddings using Subword RNNs, Yuval Pinter, Robert Guthrie and Jacob Eisenstein http://aclweb.org/anthology/D17-1010
Relational Learning and Reasoning
- Adversarial Training for Relation Extraction, Yi Wu, David Bamman and Stuart Russell https://people.eecs.berkeley.edu/~russell/papers/emnlp17-relation.pdf
Reinforcement Learning
- Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf
- Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf
- Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf
- Expected Policy Gradients, Kamil Ciosek, Shimon Whiteson, https://arxiv.org/abs/1706.05374
- Reinforcement Learning with Deep Energy-Based Policies Haarnoja et al, ICML 2017 http://proceedings.mlr.press/v70/haarnoja17a/haarnoja17a.pdf
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, Kulkarni et al., 2016, https://arxiv.org/pdf/1604.06057.pdf
- Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini et al. https://arxiv.org/pdf/1706.04972.pdf
- Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al., NIPS 2017 https://arxiv.org/abs/1706.00387
- Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf
Generation
- Generating Sentences by Editing Prototypes, Guu et al., arxiv https://arxiv.org/abs/1709.08878
- Adversarially Regularized Autoehttp://www.aclweb.org/anthology/D17-1120ncoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf
Dialog
- Reinforcement learning for spoken dialogue systems: Comparing strengths and weaknesses for practical deployment, DoD workshop, Interspeech 2006 https://pdfs.semanticscholar.org/b75b/e9388fefc44a20ceb40f6e0b916f0bbd7bbe.pdf
- Reinforcement learning for spoken dialogue systems, Singh et al., NIPS 2000 http://papers.nips.cc/paper/1775-reinforcement-learning-for-spoken-dialogue-systems.pdf
- Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management, Su et al., SIGDIAL 2017 https://arxiv.org/pdf/1707.00130.pdf
- Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning, Peng et al., EMNLP 2017. https://arxiv.org/abs/1704.03084
- Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog, Kottur et al., EMNLP best paper, https://arxiv.org/pdf/1706.08502.pdf
- A Deep Reinforcement Learning Chatbot, Serban et al., https://arxiv.org/pdf/1709.02349.pdf
- Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf
NLP for Computational Social Science
- Analyzing Language in Fake News and Political Fact-Checking, Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi https://www.cs.jhu.edu/~svitlana/papers/RCYVC_EMNLP2017.pdf
- Human Centered NLP with User Factor Adaptation. Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, H Andrew Schwartz, http://www.aclweb.org/anthology/D17-1120
