深度学习 · graph neural networks论文趋势 2023–2026

ICML/NeurIPS 深度学习方向：graph_neural_networks

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,648 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

深度学习 · graph neural networks论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
20234896814823728
2024160517610297
2025915014115297
20261023216824326

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

全部深度学习 · graph neural networks论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 1,648 篇，第 32 / 33 页

  1. NeurIPS 2023Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
  2. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
  3. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
  4. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
  5. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
  6. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
  7. NeurIPS 2023Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
  8. NeurIPS 2023Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
  9. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
  10. NeurIPS 2023Self-supervised Graph Neural Networks via Low-Rank Decomposition
  11. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
  12. NeurIPS 2023Separable Physics-Informed Neural Networks
  13. NeurIPS 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
  14. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
  15. NeurIPS 2023Sheaf Hypergraph Networks
  16. NeurIPS 2023Should Under-parameterized Student Networks Copy or Average Teacher Weights?
  17. NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
  18. NeurIPS 2023Simplicity Bias in 1-Hidden Layer Neural Networks
  19. NeurIPS 2023Simplifying Neural Network Training Under Class Imbalance
  20. NeurIPS 2023Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
  21. NeurIPS 2023Sparse Graph Learning from Spatiotemporal Time Series
  22. NeurIPS 2023Spectral Evolution and Invariance in Linear-width Neural Networks
  23. NeurIPS 2023Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
  24. NeurIPS 2023Spike-driven Transformer
  25. NeurIPS 2023Spiking PointNet: Spiking Neural Networks for Point Clouds
  26. NeurIPS 2023Squared Neural Families: A New Class of Tractable Density Models
  27. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
  28. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
  29. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
  30. NeurIPS 2023Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
  31. NeurIPS 2023Structure Learning with Adaptive Random Neighborhood Informed MCMC
  32. NeurIPS 2023Structure of universal formulas
  33. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
  34. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
  35. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
  36. NeurIPS 2023Tailoring Self-Attention for Graph via Rooted Subtrees
  37. NeurIPS 2023Task-aware Distributed Source Coding under Dynamic Bandwidth
  38. NeurIPS 2023Template-free Articulated Neural Point Clouds for Reposable View Synthesis
  39. NeurIPS 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
  40. NeurIPS 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphs
  41. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
  42. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
  43. NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
  44. NeurIPS 2023The expressive power of pooling in Graph Neural Networks
  45. NeurIPS 2023The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
  46. NeurIPS 2023The Quantization Model of Neural Scaling
  47. NeurIPS 2023The Separation Capacity of Random Neural Networks
  48. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
  49. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
  50. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations

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常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？
共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。
NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？
NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。
ICLR 2026 有多少篇深度学习 · graph neural networks论文？
ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。
ICML 2026 有多少篇深度学习 · graph neural networks论文？
ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。
CVPR 2026 有多少篇深度学习 · graph neural networks论文？
CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。
哪个会议的深度学习 · graph neural networks论文最多？
NeurIPS，2023–2026 年共 842 篇。

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