Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"

Revision as of 11:32, 8 September 2017

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
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

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

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
Adversarially Regularized Autoencoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf

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
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

@@ Line 12: / Line 12: @@
 * 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
+* Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf
 ===Learning (General)===