爱可可老师一周论文精选(2019.6.8)

No 1. 《The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial》
No 2. 《Graph Learning Network: A Structure Learning Algorithm》
No 3. 《A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends》
No 4. 《Approximate Inference Turns Deep Networks into Gaussian Processes》
No 5. 《RF-Net: An End-to-End Image Matching Network based on Receptive Field》
No 6. 《Toward Self-Supervised Object Detection in Unlabeled Videos》
No 7. 《Generative Imaging and Image Processing via Generative Encoder》
No 8. 《EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks》
No 9. 《How to Initialize your Network? Robust Initialization for WeightNorm & ResNets》
No 10. 《Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization》
No 11. 《High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks》
No 12. 《A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities》
No 13. 《Path-Augmented Graph Transformer Network》
No 14. 《What Can Neural Networks Reason About?》
No 15. 《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》
No 16. 《Luck Matters: Understanding Training Dynamics of Deep ReLU Networks》
No 17. 《Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks》
No 18. 《Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter》
No 19. 《Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae》
No 20. 《Generating Diverse High-Fidelity Images with VQ-VAE-2》
No 21. 《Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training》
No 22. 《Graph Representations for Higher-Order Logic and Theorem Proving》
No 23. 《Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology》
No 24. 《Unsupervised Paraphrasing without Translation》
No 25. 《Wasserstein Style Transfer》
No 26. 《The Strength of Structural Diversity in Online Social Networks》
No 27. 《Equivalent and Approximate Transformations of Deep Neural Networks》
No 28. 《Counting Causal Paths in Big Times Series Data on Networks》
No 29. 《Copy this Sentence》
No 30. 《Using Text Embeddings for Causal Inference》

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