深度学习 · foundation models论文趋势 2023–2026

主要结论

• 深度学习 · foundation models论文从 2023 年的 72 篇增长到 2026 年的 177 篇，三年间扩大到约 2.5 倍。
• NeurIPS 是发深度学习 · foundation models论文最多的会议，2023–2026 年共 299 篇，占四个会议总量的 56%。
• 交叉方向里涨得最快的是基础模型适配：同时属于两个主题的论文从 2023 年的 7 篇增至 2026 年的 66 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 8；同组增长最快的扩散模型是 4.1 倍，深度学习 · foundation models是 2.5 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 2026Inferotemporal Cortex Collaborates Before It Codes: Non-Serial Inter-Area Synergy in the Macaque Ventral Stream
• ICML 2026ACO-MoE-LoRA: Evolving-while-Training for Adapting Segment Anything Model 2 to Specialized Domains
• ICLR 2026Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
• CVPR 2026Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression
• NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
• ICML 2025Contrastive Localized Language-Image Pre-Training
• ICLR 2025Revisiting Convolution Architecture in the Realm of DNA Foundation Models
• CVPR 2025DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
• NeurIPS 2024$\textit{Read-ME}$: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
• ICML 2024Position: On the Societal Impact of Open Foundation Models
• CVPR 20243D-LFM: Lifting Foundation Model
• NeurIPS 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
• ICML 2023Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
• NeurIPS 2026Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations
• ICML 2026Alethia: a Foundational Encoder for Voice Deepfakes
• ICLR 2026DND: Boosting Large Language Models with Dynamic Nested Depth
• CVPR 2026UniSER A Foundation Model for Unified Soft Effects Removal
• NeurIPS 2025ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data
• ICML 2025Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
• ICLR 2025Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

全部深度学习 · foundation models论文

共 192 篇，第 2 / 4 页

51. NeurIPS 2024DEPrune: Depth-wise Separable Convolution Pruning for Maximizing GPU Parallelism
52. NeurIPS 2024Derivative-enhanced Deep Operator Network
53. NeurIPS 2024DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
54. NeurIPS 2024DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
55. NeurIPS 2024DisCEdit: Model Editing by Identifying Discriminative Components
56. NeurIPS 2024Discrete Dictionary-based Decomposition Layer for Structured Representation Learning
57. NeurIPS 2024Do Multimodal Foundation Models Understand Enterprise Workflows? A Benchmark for Business Process Management Tasks
58. NeurIPS 2024DOFEN: Deep Oblivious Forest ENsemble
59. NeurIPS 2024ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
60. NeurIPS 2024einspace: Searching for Neural Architectures from Fundamental Operations
61. NeurIPS 2024ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
62. NeurIPS 2024Elliptical Attention
63. NeurIPS 2024Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View
64. NeurIPS 2024Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
65. NeurIPS 2024Facilitating Multimodal Classification via Dynamically Learning Modality Gap
66. NeurIPS 2024FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing
67. NeurIPS 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
68. NeurIPS 2024FilterNet: Harnessing Frequency Filters for Time Series Forecasting
69. NeurIPS 2024FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
70. NeurIPS 2024Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
71. NeurIPS 2024From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
72. NeurIPS 2024From Similarity to Superiority: Channel Clustering for Time Series Forecasting
73. NeurIPS 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
74. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
75. NeurIPS 2024Group and Shuffle: Efficient Structured Orthogonal Parametrization
76. NeurIPS 2024How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
77. NeurIPS 2024Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers
78. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt
79. NeurIPS 2024Improving Neural ODE Training with Temporal Adaptive Batch Normalization
80. NeurIPS 2024In Pursuit of Causal Label Correlations for Multi-label Image Recognition
81. NeurIPS 2024Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery
82. NeurIPS 2024Infinite-Dimensional Feature Interaction
83. NeurIPS 2024Initializing Variable-sized Vision Transformers from Learngene with Learnable Transformation
84. NeurIPS 2024Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
85. NeurIPS 2024Is Multiple Object Tracking a Matter of Specialization?
86. NeurIPS 2024Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
87. NeurIPS 2024L-TTA: Lightweight Test-Time Adaptation Using a Versatile Stem Layer
88. NeurIPS 2024LaSCal: Label-Shift Calibration without target labels
89. NeurIPS 2024Latent Functional Maps: a spectral framework for representation alignment
90. NeurIPS 2024Learning diverse causally emergent representations from time series data
91. NeurIPS 2024Learning from Highly Sparse Spatio-temporal Data
92. NeurIPS 2024Learning Group Actions on Latent Representations
93. NeurIPS 2024Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
94. NeurIPS 2024Learning symmetries via weight-sharing with doubly stochastic tensors
95. NeurIPS 2024Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
96. NeurIPS 2024Linearly Decomposing and Recomposing Vision Transformers for Diverse-Scale Models
97. NeurIPS 2024LinNet: Linear Network for Efficient Point Cloud Representation Learning
98. NeurIPS 2024LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems
99. NeurIPS 2024MambaTree: Tree Topology is All You Need in State Space Model
100. NeurIPS 2024Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · foundation models论文？

共 536 篇，按年份分别是 2023 年 72 篇、2024 年 209 篇、2025 年 78 篇、2026 年 177 篇。

NeurIPS 2026 有多少篇深度学习 · foundation models论文？

NeurIPS 2026 有 27 篇深度学习 · foundation models论文，上一届是 15 篇。

ICLR 2026 有多少篇深度学习 · foundation models论文？

ICLR 2026 有 8 篇深度学习 · foundation models论文，上一届是 6 篇。

ICML 2026 有多少篇深度学习 · foundation models论文？

ICML 2026 有 135 篇深度学习 · foundation models论文，上一届是 53 篇。

CVPR 2026 有多少篇深度学习 · foundation models论文？

CVPR 2026 有 7 篇深度学习 · foundation models论文，上一届是 4 篇。

哪个会议的深度学习 · foundation models论文最多？

NeurIPS，2023–2026 年共 299 篇。