No 1. 《Rethinking Bias-Variance Trade-off for Generalization of Neural Networks》
    No 2. 《Generalization and Representational Limits of Graph Neural Networks》
    No 3. 《Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks》
    No 4. 《How Much Knowledge Can You Pack Into the Parameters of a Language Model?》
    No 5. 《CausalML: Python Package for Causal Machine Learning》
    No 6. 《Bayesian Deep Learning and a Probabilistic Perspective of Generalization》
    No 7. 《A Primer in BERTology: What we know about how BERT works》
    No 8. 《MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers》
    No 9. 《t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections》
    No 10. 《Multivariate time-series modeling with generative neural networks》
    No 11. 《Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning》
    No 12. 《Joint Embedding in Named Entity Linking on Sentence Level》
    No 13. 《Fast Differentiable Sorting and Ranking》
    No 14. 《Affinity and Diversity: Quantifying Mechanisms of Data Augmentation》
    No 15. 《Gradient Boosting Neural Networks: GrowNet》
    No 16. 《Bayesian Computing in the Statistics and Data Science Curriculum》
    No 17. 《PolyGen: An Autoregressive Generative Model of 3D Meshes》
    No 18. 《T-Net: A Template-Supervised Network for Task-specific Feature Extraction in Biomedical Image Analysis》
    No 19. 《Few-shot Natural Language Generation for Task-Oriented Dialog》
    No 20. 《Freeze Discriminator: A Simple Baseline for Fine-tuning GANs》
    No 21. 《Batch Normalization Biases Deep Residual Networks Towards Shallow Paths》
    No 22. 《Neural Network Compression Framework for fast model inference》
    No 23. 《Adversarial Machine Learning — Industry Perspectives》
    No 24. 《Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-Layer Networks》
    No 25. 《Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent》
    No 26. 《BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images》
    No 27. 《Do We Need Zero Training Loss After Achieving Zero Training Error?》
    No 28. 《Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach》
    No 29. 《Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image》
    No 30. 《A Hierarchy of Limitations in Machine Learning》