Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"
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| − | *A Brief Survey of Deep Reinforcement Learning, Arulkumaran et al., IEEE Signal Processing Magazine 2017 https://arxiv.org/pdf/1708.05866.pdf | + | ===Relational Learning and Reasoning=== |
| − | * | + | * Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf |
| − | *Reinforcement Learning with Deep Energy-Based Policies Haarnoja et al, ICML 2017 http://proceedings.mlr.press/v70/haarnoja17a/haarnoja17a.pdf | + | * A simple neural network module for relational reasoning, Santoro et al., Arxiv https://arxiv.org/abs/1706.01427 |
| − | *Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf | + | |
| + | ===Reinforcement Learning=== | ||
| + | * A Brief Survey of Deep Reinforcement Learning, Arulkumaran et al., IEEE Signal Processing Magazine 2017 https://arxiv.org/pdf/1708.05866.pdf | ||
| + | * 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 | ||
| + | * 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 | ||
| + | * 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 | * Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini et al. https://arxiv.org/pdf/1706.04972.pdf | ||
| − | * | + | |
| − | * | + | ===Learning (General)=== |
| + | * An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098 | ||
| + | * Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.pdf | ||
*The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf | *The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf | ||
| + | |||
| + | ===Generation=== | ||
| + | * Multi-Task Video Captioning with Video and Entailment Generation, ACL 2017 Outstanding Paper https://arxiv.org/pdf/1704.07489.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 | ||
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| − | |||
| − | |||
| − | |||
* Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 https://arxiv.org/pdf/1706.03850.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 | * Adversarially Regularized Autoencoders 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 | * 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 | * 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 | ||
| + | * Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.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 17:09, 4 September 2017
Contents
Relational Learning and Reasoning
- Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf
- A simple neural network module for relational reasoning, Santoro et al., Arxiv https://arxiv.org/abs/1706.01427
Reinforcement Learning
- A Brief Survey of Deep Reinforcement Learning, Arulkumaran et al., IEEE Signal Processing Magazine 2017 https://arxiv.org/pdf/1708.05866.pdf
- 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
- 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
- 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
Learning (General)
- An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098
- Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.pdf
- The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf
Generation
- Multi-Task Video Captioning with Video and Entailment Generation, ACL 2017 Outstanding Paper https://arxiv.org/pdf/1704.07489.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 https://arxiv.org/pdf/1706.03850.pdf
- Adversarially Regularized Autoencoders 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
- Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf
- Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf
