No 1. 【具有周期激活函数的隐式神经网络表示】
    No 2. 《An Algorithmic Introduction to Clustering》
    No 3. 《What Do Neural Networks Learn When Trained With Random Labels?》
    No 4. 《Rethinking Pre-training and Self-training》
    No 5. 《Deep Stock Predictions》
    No 6. 《Anomaly Detection with Domain Adaptation》
    No 7. 《A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions》
    No 8. 《Self-Supervised Relational Reasoning for Representation Learning》
    No 9. 《Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies》
    No 10. 《Training Generative Adversarial Networks with Limited Data》
    No 11. 《All Local Minima are Global for Two-Layer ReLU Neural Networks: The Hidden Convex Optimization Landscape》
    No 12. 《Self-supervised Learning on Graphs: Deep Insights and New Direction》
    No 13. 《Noise or Signal: The Role of Image Backgrounds in Object Recognition》
    No 14. 《Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild》
    No 15. 《Big Self-Supervised Models are Strong Semi-Supervised Learners》
    No 16. 《Disentangled Non-Local Neural Networks》
    No 17. 《Is deep learning necessary for simple classification tasks?》
    No 18. 《Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective》
    No 19. 《Sparse and Continuous Attention Mechanisms》
    No 20. 《Super-resolution Variational Auto-Encoders》
    No 21. 《Temporal Graph Networks for Deep Learning on Dynamic Graphs》
    No 22. 《Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks》
    No 23. 《Variational Auto-Regressive Gaussian Processes for Continual Learning》
    No 24. 《Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning》
    No 25. 《LSD-C: Linearly Separable Deep Clusters》
    No 26. 《Monte Carlo Gradient Estimation in Machine Learning》
    No 27. 【PULSE：生成模型潜空间探索自监督照片上采样(用降采样损失训练超分辨率模型)】
    No 28. 《Wavelet Networks: Scale Equivariant Learning From Raw Waveforms》
    No 29. 《An overall view of key problems in algorithmic trading and recent progress》
    No 30. 《Why Normalizing Flows Fail to Detect Out-of-Distribution Data》