热点｜最近7天最受欢迎的20篇热门AI论文
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/