数据集与基准论文趋势 2023–2026

新数据集与评测协议

统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 5,856 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

数据集与基准论文数,按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023227162166382937
20244482163183641,346
20256374191893371,582
20266236352754581,991

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

全部数据集与基准论文

共 318 篇,第 5 / 7 页

  1. ICML 2024NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors
  2. ICML 2024Neuro-Symbolic Temporal Point Processes
  3. ICML 2024New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering
  4. ICML 2024New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming
  5. ICML 2024Non-clairvoyant Scheduling with Partial Predictions
  6. ICML 2024O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
  7. ICML 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
  8. ICML 2024On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning
  9. ICML 2024On the Last-Iterate Convergence of Shuffling Gradient Methods
  10. ICML 2024On the Nonlinearity of Layer Normalization
  11. ICML 2024On The Statistical Complexity of Offline Decision-Making
  12. ICML 2024On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective
  13. ICML 2024One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
  14. ICML 2024OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
  15. ICML 2024Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
  16. ICML 2024Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
  17. ICML 2024OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
  18. ICML 2024OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
  19. ICML 2024OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport
  20. ICML 2024OTMatch: Improving Semi-Supervised Learning with Optimal Transport
  21. ICML 2024Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
  22. ICML 2024Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
  23. ICML 2024PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
  24. ICML 2024Perturb-and-Project: Differentially Private Similarities and Marginals
  25. ICML 2024Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach
  26. ICML 2024Plug-and-Play image restoration with Stochastic deNOising REgularization
  27. ICML 2024PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels
  28. ICML 2024Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction
  29. ICML 2024Position: A Roadmap to Pluralistic Alignment
  30. ICML 2024Position: A Safe Harbor for AI Evaluation and Red Teaming
  31. ICML 2024Position: AI/ML Influencers Have a Place in the Academic Process
  32. ICML 2024Position: Amazing Things Come From Having Many Good Models
  33. ICML 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
  34. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
  35. ICML 2024Position: Is machine learning good or bad for the natural sciences?
  36. ICML 2024Position: Measure Dataset Diversity, Don't Just Claim It
  37. ICML 2024Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
  38. ICML 2024Position: Topological Deep Learning is the New Frontier for Relational Learning
  39. ICML 2024Position: TrustLLM: Trustworthiness in Large Language Models
  40. ICML 2024Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
  41. ICML 2024Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
  42. ICML 2024Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
  43. ICML 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
  44. ICML 2024Prodigy: An Expeditiously Adaptive Parameter-Free Learner
  45. ICML 2024Provable Contrastive Continual Learning
  46. ICML 2024Provably Scalable Black-Box Variational Inference with Structured Variational Families
  47. ICML 2024PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
  48. ICML 2024Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
  49. ICML 2024QuIP$\#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
  50. ICML 2024ReconBoost: Boosting Can Achieve Modality Reconcilement

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查数据集与基准在 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": "dataset-and-benchmarks"}

两分钟接入 Paperlist MCP(Claude Code、Cursor、Codex、ChatGPT)

常见问题

2023–2026 年四大会议一共有多少篇数据集与基准论文?
共 5,856 篇,按年份分别是 2023 年 937 篇、2024 年 1,346 篇、2025 年 1,582 篇、2026 年 1,991 篇。
NeurIPS 2026 有多少篇数据集与基准论文?
NeurIPS 2026 有 623 篇数据集与基准论文,上一届是 637 篇。
ICLR 2026 有多少篇数据集与基准论文?
ICLR 2026 有 635 篇数据集与基准论文,上一届是 419 篇。
ICML 2026 有多少篇数据集与基准论文?
ICML 2026 有 275 篇数据集与基准论文,上一届是 189 篇。
CVPR 2026 有多少篇数据集与基准论文?
CVPR 2026 有 458 篇数据集与基准论文,上一届是 337 篇。
哪个会议的数据集与基准论文最多?
NeurIPS,2023–2026 年共 1,935 篇。

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