No 1. 《Time2Vec: Learning a Vector Representation of Time》
    No 2. 《SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications》
    No 3. 《Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function》
    No 4. 《Techniques for Automated Machine Learning》
    No 5. 《A Baseline for 3D Multi-Object Tracking》
    No 6. 《MixNet: Mixed Depthwise Convolutional Kernels》
    No 7. 《Pairwise Link Prediction》
    No 8. 《Quant GANs: Deep Generation of Financial Time Series》
    No 9. 《Green AI》
    No 10. 《Deep-SLAM++: Object-level RGBD SLAM based on class-specific deep shape priors》
    No 11. 《NPA: Neural News Recommendation with Personalized Attention》
    No 12. 《OmniNet: A unified architecture for multi-modal multi-task learning》
    No 13. 《Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation》
    No 14. 《Faster Neural Network Training with Data Echoing》
    No 15. 《Image-and-Spatial Transformer Networks for Structure-Guided Image Registration》
    No 16. 《Bayesian Inference with Generative Adversarial Network Priors》
    No 17. 《Noise Contrastive Variational Autoencoders》
    No 18. 《Understanding Video Content: Efficient Hero Detection and Recognition for the Game ‘Honor of Kings’》
    No 19. 《U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation》
    No 20. 《Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images》
    No 21. 《Spectral Analysis of Latent Representations》
    No 22. 《IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification》
    No 23. 《An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments》
    No 24. 《Differentiable Bayesian Neural Network Inference for Data Streams》
    No 25. 《Trusses and Trapezes: Easily-Interpreted Communities in Social Networks》
    No 26. 《Y-Autoencoders: disentangling latent representations via sequential-encoding》
    No 27. 《Convolutional Neural Networks on Randomized Data》
    No 28. 《Learning to Select, Track, and Generate for Data-to-Text》
    No 29. 《Hierarchical Sequence to Sequence Voice Conversion with Limited Data》
    No 30. 《Statistical data analysis in the Wasserstein space》