Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"
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* An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098 | * 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 | * 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 | ||
Revision as of 16:23, 6 July 2017
- MUSE: Modularizing Unsupervised Sense Embeddings, Lee and Chen, EMNLP 2017 https://arxiv.org/pdf/1704.04601.pdf
- Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf
- Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf
- Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper
- 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
- Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini et al. https://arxiv.org/pdf/1706.04972.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
