Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"
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*Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf | *Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf | ||
*Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf | *Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf | ||
| + | *The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf | ||
* Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf | * Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf | ||
*Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf | *Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf | ||
Revision as of 11:52, 4 September 2017
- A Brief Survey of Deep Reinforcement Learning, Arulkumaran et al., IEEE Signal Processing Magazine 2017 https://arxiv.org/pdf/1708.05866.pdf
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, Kulkarni et al., 2016, https://arxiv.org/pdf/1604.06057.pdf
- Reinforcement Learning with Deep Energy-Based Policies Haarnoja et al, ICML 2017 http://proceedings.mlr.press/v70/haarnoja17a/haarnoja17a.pdf
- Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf
- Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini et al. https://arxiv.org/pdf/1706.04972.pdf
- Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf
- Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf
- The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf
- Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf
- Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf
- Multi-Task Video Captioning with Video and Entailment Generation, ACL 2017 Outstanding Paper https://arxiv.org/pdf/1704.07489.pdf
- A simple neural network module for relational reasoning, Santoro et al., Arxiv https://arxiv.org/abs/1706.01427
- Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog, Kottur et al., EMNLP best paper, https://arxiv.org/pdf/1706.08502.pdf
- Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 https://arxiv.org/pdf/1706.03850.pdf
- Adversarially Regularized Autoencoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf
- An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098
- Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf
- Hindsight Experience Replay, Andrychowicz et al, https://arxiv.org/pdf/1707.01495.pdf
- Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.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
