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 728 篇，第 11 / 15 页

  1. NeurIPS 2023Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
  2. NeurIPS 2023Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
  3. NeurIPS 2023Learning Large Graph Property Prediction via Graph Segment Training
  4. NeurIPS 2023Learning Large-scale Neural Fields via Context Pruned Meta-Learning
  5. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
  6. NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
  7. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
  8. NeurIPS 2023Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
  9. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
  10. NeurIPS 2023Learning threshold neurons via edge of stability
  11. NeurIPS 2023Learning To Dive In Branch And Bound
  12. NeurIPS 2023Learning Trajectories are Generalization Indicators
  13. NeurIPS 2023Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
  14. NeurIPS 2023Leveraging the two-timescale regime to demonstrate convergence of neural networks
  15. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
  16. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
  17. NeurIPS 2023Locality-Aware Generalizable Implicit Neural Representation
  18. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
  19. NeurIPS 2023Long Sequence Hopfield Memory
  20. NeurIPS 2023Lovász Principle for Unsupervised Graph Representation Learning
  21. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
  22. NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
  23. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
  24. NeurIPS 2023Max-Sliced Mutual Information
  25. NeurIPS 2023Maximum Independent Set: Self-Training through Dynamic Programming
  26. NeurIPS 2023MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
  27. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
  28. NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
  29. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
  30. NeurIPS 2023Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
  31. NeurIPS 2023MLFMF: Data Sets for Machine Learning for Mathematical Formalization
  32. NeurIPS 2023MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
  33. NeurIPS 2023Modulated Neural ODEs
  34. NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
  35. NeurIPS 2023Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
  36. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
  37. NeurIPS 2023Multiplication-Free Transformer Training via Piecewise Affine Operations
  38. NeurIPS 2023MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
  39. NeurIPS 2023NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning
  40. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
  41. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
  42. NeurIPS 2023NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
  43. NeurIPS 2023Network Regression with Graph Laplacians
  44. NeurIPS 2023Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
  45. NeurIPS 2023Neural (Tangent Kernel) Collapse
  46. NeurIPS 2023Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
  47. NeurIPS 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
  48. NeurIPS 2023Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
  49. NeurIPS 2023Neural Functional Transformers
  50. NeurIPS 2023Neural Graph Generation from Graph Statistics

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用 paperlist 查深度学习 · graph neural networks在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "dl-graph-neural-networks"}

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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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