No 1. 《Object Detection in 20 Years: A Survey》
    No 2. 【视频三维人体动态学习】
    No 3. 《Learning Embeddings into Entropic Wasserstein Spaces》
    No 4. 《Bayesian Optimization using Deep Gaussian Processes》
    No 5. 《Embedding Human Knowledge in Deep Neural Network via Attention Map》
    No 6. 《D2-Net: A Trainable CNN for Joint Detection and Description of Local Features》
    No 7. 《Graph U-Nets》
    No 8. 《Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks》
    No 9. 《Graph Convolutional Gaussian Processes》
    No 10. 《Targeted Sentiment Analysis: A Data-Driven Categorization》
    No 11. 《S4L: Self-Supervised Semi-Supervised Learning》
    No 12. 《Learning Loss for Active Learning》
    No 13. 《Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting》
    No 14. 《MASS: Masked Sequence to Sequence Pre-training for Language Generation》
    No 15. 【人体姿态可学习三角测量】
    No 16. 《Zero-Shot Voice Style Transfer with Only Autoencoder Loss》
    No 17. 《Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs》
    No 18. 《Meta-Learning with Differentiable Convex Optimization》
    No 19. 《MeshCNN: A Network with an Edge》
    No 20. 《Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping》
    No 21. 《RadiX-Net: Structured Sparse Matrices for Deep Neural Networks》
    No 22. 《Interdisciplinary Relationships Between Biological and Physical Sciences》
    No 23. 《Interactive Image Generation Using Scene Graphs》
    No 24. 《Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information》
    No 25. 《Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection — An Analysis on CIC-AWS-2018 dataset》
    No 26. 《Structural Equation Modeling using Computation Graphs》
    No 27. 《Machine Learning at Microsoft with ML .NET》
    No 28. 《AutoAssist: A Framework to Accelerate Training of Deep Neural Networks》
    No 29. 《Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data》
    No 30. 《Learning to Interpret Satellite Images in Global Scale Using Wikipedia》