Fall 2017 CS595I Advanced NLP/ML Seminar

• 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
• 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
• 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