热点｜最近7天最受欢迎的20篇热门AI论文

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1. 《Toward Geometric Deep SLAM》

https://arxiv.org/abs/1707.07410

2. 《DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning》

https://arxiv.org/abs/1707.06690

https://github.com/xwhan/DeepPath

3. 《Stock Prediction: a method based on extraction of news features and recurrent neural networks》

https://arxiv.org/abs/1707.07585

4. 《A Distributional Perspective on Reinforcement Learning》

https://deepmind.com/blog/going-beyond-average-reinforcement-learning/

https://arxiv.org/abs/1707.06887

5. 《Memory-Efficient Implementation of DenseNets》

https://arxiv.org/abs/1707.06990

https://github.com/liuzhuang13/DenseNet/tree/master/models

https://github.com/gpleiss/efficient_densenet_pytorch

https://github.com/Tongcheng/DN_CaffeScript

6. 《graph2vec: Learning Distributed Representations of Graphs》

https://arxiv.org/abs/1707.05005

https://sites.google.com/view/graph2vec

7. 《Learning from Simulated and Unsupervised Images through Adversarial Training》

https://arxiv.org/abs/1612.07828

8. 《Annotating Object Instances with a Polygon-RNN》

https://arxiv.org/abs/1704.05548

http://www.cs.toronto.edu/polyrnn/

9. 《Convolutional neural network architecture for geometric matching》

https://arxiv.org/abs/1703.05593

https://github.com/ignacio-rocco/cnngeometric_matconvnet

http://www.di.ens.fr/willow/research/cnngeometric/

10. 《Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions》

https://link.springer.com/article/10.1007/s10278-017-9983-4

11. 《DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging》

https://arxiv.org/abs/1707.04596

12. 《Semantic Segmentation with Reverse Attention》

https://arxiv.org/abs/1707.06426

13. 《Designing Neural Network Architectures using Reinforcement Learning》

https://arxiv.org/abs/1611.02167

https://bowenbaker.github.io/metaqnn/

https://github.com/bowenbaker/metaqnn

14. 《Large-scale Multiview 3D Hand Pose Dataset》

https://arxiv.org/abs/1707.03742

http://www.rovit.ua.es/dataset/mhpdataset/

15. 《Semantic Segmentation using Adversarial Networks》

https://arxiv.org/abs/1611.08408

https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks

16. 《Deep Learning in Robotics: A Review of Recent Research》

https://arxiv.org/abs/1707.07217

17. 《Variational Approaches for Auto-Encoding Generative Adversarial Networks》

https://arxiv.org/abs/1706.04987

https://github.com/victor-shepardson/alpha-GAN

18. 《A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors》

https://arxiv.org/abs/1707.03979

19. 《Dual Path Networks》

https://arxiv.org/abs/1707.01629

https://github.com/oyam/pytorch-DPNs

20. 《Challenges of Data-to-Document Generation》
https://arxiv.org/abs/1707.08052

http://lstm.seas.harvard.edu/docgen/

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