特征归因论文趋势 2023–2026

主要结论

• 特征归因论文从 2023 年的 118 篇增长到 2026 年的 219 篇，三年间扩大到约 1.9 倍。
• NeurIPS 是发特征归因论文最多的会议，2023–2026 年共 193 篇，占四个会议总量的 28%。
• 交叉方向里涨得最快的是理论 · learning theory：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 14 篇。
• 在「可解释性」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的机制解释是 8.1 倍，特征归因是 1.9 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization
• ICML 2026Activation with Intrinsic-Extrinsic Consensus
• ICLR 2026AnyUp: Universal Feature Upsampling
• CVPR 2026A Training-Free Style-Personalization via SVD-Based Feature Decomposition
• NeurIPS 2025Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks
• ICML 2025BCE vs. CE in Deep Feature Learning
• ICLR 2025A Differentiable Rank-Based Objective for Better Feature Learning
• CVPR 2025A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
• NeurIPS 2024$\texttt{dattri}$: A Library for Efficient Data Attribution
• ICML 2024Asymptotics of feature learning in two-layer networks after one gradient-step
• ICLR 2024An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks
• CVPR 2024DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning
• NeurIPS 2023$\mathcal{M}^4$: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
• ICML 2023Decoding Layer Saliency in Language Transformers
• ICLR 2023Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
• CVPR 2023A Practical Upper Bound for the Worst-Case Attribution Deviations
• NeurIPS 2026Adaptive Feature Propagation for Attribute-Missing Graph Clustering
• ICML 2026Aggregate Models, Not Explanations: Improving Feature Importance Estimation
• ICLR 2026Attribution-Guided Decoding
• CVPR 2026FBTA Enabling Single-GPU End-to-End Gigapixel WSI Classification with Feature Bridging

全部特征归因论文

共 29 篇，第 1 / 1 页

1. ICLR 2023Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
2. ICLR 2023Composing Task Knowledge With Modular Successor Feature Approximators
3. ICLR 2023Confidence-Based Feature Imputation for Graphs with Partially Known Features
4. ICLR 2023Contrastive Corpus Attribution for Explaining Representations
5. ICLR 2023CoRTX: Contrastive Framework for Real-time Explanation
6. ICLR 2023Explaining RL Decisions with Trajectories
7. ICLR 2023Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
8. ICLR 2023Feature selection and low test error in shallow low-rotation ReLU networks
9. ICLR 2023FedFA: Federated Feature Augmentation
10. ICLR 2023Fooling SHAP with Stealthily Biased Sampling
11. ICLR 2023Function-Consistent Feature Distillation
12. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
13. ICLR 2023Inequality phenomenon in $l_{\infty}$-adversarial training, and its unrealized threats
14. ICLR 2023Interpretability with full complexity by constraining feature information
15. ICLR 2023Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering
16. ICLR 2023Learning Multimodal Data Augmentation in Feature Space
17. ICLR 2023Long-Tailed Learning Requires Feature Learning
18. ICLR 2023MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
19. ICLR 2023Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
20. ICLR 2023One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks
21. ICLR 2023Over-Training with Mixup May Hurt Generalization
22. ICLR 2023PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
23. ICLR 2023Personalized Federated Learning with Feature Alignment and Classifier Collaboration
24. ICLR 2023Predictive Inference with Feature Conformal Prediction
25. ICLR 2023Re-calibrating Feature Attributions for Model Interpretation
26. ICLR 2023Sequential Attention for Feature Selection
27. ICLR 2023Temporal Dependencies in Feature Importance for Time Series Prediction
28. ICLR 2023The Curious Case of Benign Memorization
29. ICLR 2023The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes

常见问题

2023–2026 年四大会议一共有多少篇特征归因论文？

共 695 篇，按年份分别是 2023 年 118 篇、2024 年 168 篇、2025 年 190 篇、2026 年 219 篇。

NeurIPS 2026 有多少篇特征归因论文？

NeurIPS 2026 有 61 篇特征归因论文，上一届是 57 篇。

ICLR 2026 有多少篇特征归因论文？

ICLR 2026 有 53 篇特征归因论文，上一届是 42 篇。

ICML 2026 有多少篇特征归因论文？

ICML 2026 有 56 篇特征归因论文，上一届是 51 篇。

CVPR 2026 有多少篇特征归因论文？

CVPR 2026 有 49 篇特征归因论文，上一届是 40 篇。

哪个会议的特征归因论文最多？

NeurIPS，2023–2026 年共 193 篇。