爱可可老师一周论文精选(2019.7.6)

No 1. 《Modern Deep Reinforcement Learning Algorithms》
No 2. 《A Survey on GANs for Anomaly Detection》
No 3. 《Human vs Machine Attention in Neural Networks: A Comparative Study》
No 4. 《A Fourier Perspective on Model Robustness in Computer Vision》
No 5. 《Selection Via Proxy: Efficient Data Selection For Deep Learning》
No 6. 《Machine Learning Testing: Survey, Landscapes and Horizons》
No 7. 《On-Device Neural Net Inference with Mobile GPUs》
No 8. 《Causal models on probability spaces》
No 9. 《Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection》
No 10. 《DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints》
No 11. 《Augmenting Self-attention with Persistent Memory》
No 12. 《Semi-supervised Image Attribute Editing using Generative Adversarial Networks》
No 13. 《Neural Machine Reading Comprehension: Methods and Trends》
No 14. 《Inspirational Adversarial Image Generation》
No 15. 《Benign Overfitting in Linear Regression》
No 16. 《Benchmarking Model-Based Reinforcement Learning》
No 17. 《Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection》
No 18. 《GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation》
No 19. 《Fine-grained zero-shot recognition with metric rescaling》
No 20. 《The Difficulty of Training Sparse Neural Networks》
No 21. 《Brno Mobile OCR Dataset》
No 22. 《A Simple Deep Personalized Recommendation System》
No 23. 《Fast Training of Sparse Graph Neural Networks on Dense Hardware》
No 24. 《Learning Data Augmentation Strategies for Object Detection》
No 25. 《Complexity of Highly Parallel Non-Smooth Convex Optimization》
No 26. 《Attribute-Driven Spontaneous Motion in Unpaired Image Translation》
No 27. 《Style Generator Inversion for Image Enhancement and Animation》
No 28. 《Identifying Emotions from Walking using Affective and Deep Features》
No 29. 《Discovering Communities of Community Discovery》
No 30. 《Supervised Uncertainty Quantification for Segmentation with Multiple Annotations》

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