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 50 篇，第 1 / 1 页

1. NeurIPS 2024$\texttt{dattri}$: A Library for Efficient Data Attribution
2. NeurIPS 2024Benchmarking the Attribution Quality of Vision Models
3. NeurIPS 2024Credit Attribution and Stable Compression
4. NeurIPS 2024Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables
5. NeurIPS 2024DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
6. NeurIPS 2024Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model
7. NeurIPS 2024DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
8. NeurIPS 2024Dimension-free deterministic equivalents and scaling laws for random feature regression
9. NeurIPS 2024Efficient Sketches for Training Data Attribution and Studying the Loss Landscape
10. NeurIPS 2024Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
11. NeurIPS 2024EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature
12. NeurIPS 2024ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
13. NeurIPS 2024Explanations that reveal all through the definition of encoding
14. NeurIPS 2024Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations
15. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
16. NeurIPS 2024FUGAL: Feature-fortified Unrestricted Graph Alignment
17. NeurIPS 2024Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
18. NeurIPS 2024Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation
19. NeurIPS 2024Infinite-Dimensional Feature Interaction
20. NeurIPS 2024Is Function Similarity Over-Engineered? Building a Benchmark
21. NeurIPS 2024Learning Low-Rank Feature for Thorax Disease Classification
22. NeurIPS 2024Learning Successor Features the Simple Way
23. NeurIPS 2024MatrixNet: Learning over symmetry groups using learned group representations
24. NeurIPS 2024MemoryFormer : Minimize Transformer Computation by Removing Fully-Connected Layers
25. NeurIPS 2024Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks
26. NeurIPS 2024Most Influential Subset Selection: Challenges, Promises, and Beyond
27. NeurIPS 2024Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization
28. NeurIPS 2024NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
29. NeurIPS 2024On Feature Learning in Structured State Space Models
30. NeurIPS 2024On the Efficiency of ERM in Feature Learning
31. NeurIPS 2024On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
32. NeurIPS 2024Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
33. NeurIPS 2024Parallel Backpropagation for Shared-Feature Visualization
34. NeurIPS 2024Personalized Federated Learning via Feature Distribution Adaptation
35. NeurIPS 2024Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
36. NeurIPS 2024Provable Benefit of Cutout and CutMix for Feature Learning
37. NeurIPS 2024ProxyFusion: Face Feature Aggregation Through Sparse Experts
38. NeurIPS 2024shapiq: Shapley Interactions for Machine Learning
39. NeurIPS 2024Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
40. NeurIPS 2024SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis
41. NeurIPS 2024Symmetries in Overparametrized Neural Networks: A Mean Field View
42. NeurIPS 2024The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
43. NeurIPS 2024The Prevalence of Neural Collapse in Neural Multivariate Regression
44. NeurIPS 2024To Learn or Not to Learn, That is the Question — A Feature-Task Dual Learning Model of Perceptual Learning
45. NeurIPS 2024Training Data Attribution via Approximate Unrolling
46. NeurIPS 2024Understanding Visual Feature Reliance through the Lens of Complexity
47. NeurIPS 2024Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
48. NeurIPS 2024WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
49. NeurIPS 2024Where Do Large Learning Rates Lead Us?
50. NeurIPS 2024You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection

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。