No 1. 【深度学习电影接吻镜头检测器，2.3TB数据集，已标注100部电影包含263个接吻片段和363个非接吻片段 😗】
    No 2. 《Attention Is (not) All You Need for Commonsense Reasoning》
    No 3. 《Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations》
    No 4. 《Is Attention Interpretable?》
    No 5. 《What Does BERT Look At? An Analysis of BERT’s Attention》
    No 6. 【听“音”知“形”：根据语音预测(个人风格)手势，大型特定人手势视频数据集(10人/128小时)】
    No 7. 《Generative Adversarial Networks: A Survey and Taxonomy》
    No 8. 《Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning》
    No 9. 《Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference》
    No 10. 《Rates of Convergence for Sparse Variational Gaussian Process Regression》
    No 11. 《Understanding Generalization through Visualizations》
    No 12. 《2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019》
    No 13. 《DiCENet: Dimension-wise Convolutions for Efficient Networks》
    No 14. 《WikiDataSets : Standardized sub-graphs from WikiData》
    No 15. 《Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift》
    No 16. 《Automated Machine Learning: State-of-The-Art and Open Challenges》
    No 17. 《Efficient Forward Architecture Search》
    No 18. 《Selfie: Self-supervised Pretraining for Image Embedding》
    No 19. 《How to make a pizza: Learning a compositional layer-based GAN model》
    No 20. 《Deep learning for image segmentation-a short survey》
    No 21. 《Particle Filter Recurrent Neural Networks》
    No 22. 《Meta-Learning via Learned Loss》
    No 23. 《Shapes and Context: In-the-Wild Image Synthesis & Manipulation》
    No 24. 《Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network》
    No 25. 《When to use parametric models in reinforcement learning?》
    No 26. 《AutoGrow: Automatic Layer Growing in Deep Convolutional Networks》
    No 27. 《Causal Discovery with Reinforcement Learning》
    No 28. 《A Closer Look at the Optimization Landscapes of Generative Adversarial Networks》
    No 29. 《Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View》
    No 30. 《Learning Sparse Networks Using Targeted Dropout》