深度学习 · foundation models论文趋势 2023–2026
ICML/NeurIPS 深度学习方向:foundation_models
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 536 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 65 | 0 | 7 | 0 | 72 |
| 2024 | 192 | 0 | 11 | 6 | 209 |
| 2025 | 15 | 6 | 53 | 4 | 78 |
| 2026 | 27 | 8 | 135 | 7 | 177 |
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 209 篇,第 3 / 5 页
- NeurIPS 2024Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
- NeurIPS 2024Is Multiple Object Tracking a Matter of Specialization?
- NeurIPS 2024Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
- NeurIPS 2024L-TTA: Lightweight Test-Time Adaptation Using a Versatile Stem Layer
- NeurIPS 2024LaSCal: Label-Shift Calibration without target labels
- NeurIPS 2024Latent Functional Maps: a spectral framework for representation alignment
- NeurIPS 2024Learning diverse causally emergent representations from time series data
- NeurIPS 2024Learning from Highly Sparse Spatio-temporal Data
- NeurIPS 2024Learning Group Actions on Latent Representations
- NeurIPS 2024Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
- NeurIPS 2024Learning symmetries via weight-sharing with doubly stochastic tensors
- NeurIPS 2024Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
- NeurIPS 2024Linearly Decomposing and Recomposing Vision Transformers for Diverse-Scale Models
- NeurIPS 2024LinNet: Linear Network for Efficient Point Cloud Representation Learning
- NeurIPS 2024LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems
- NeurIPS 2024MambaTree: Tree Topology is All You Need in State Space Model
- NeurIPS 2024Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages
- NeurIPS 2024MatFormer: Nested Transformer for Elastic Inference
- NeurIPS 2024MatrixNet: Learning over symmetry groups using learned group representations
- NeurIPS 2024Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
- NeurIPS 2024MemoryFormer : Minimize Transformer Computation by Removing Fully-Connected Layers
- NeurIPS 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
- NeurIPS 2024Meta-Learning Universal Priors Using Non-Injective Change of Variables
- NeurIPS 2024MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
- NeurIPS 2024Mixtures of Experts for Audio-Visual Learning
- NeurIPS 2024MoEUT: Mixture-of-Experts Universal Transformers
- NeurIPS 2024MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts
- NeurIPS 2024Monomial Matrix Group Equivariant Neural Functional Networks
- NeurIPS 2024MoVA: Adapting Mixture of Vision Experts to Multimodal Context
- NeurIPS 2024Multi-Head Mixture-of-Experts
- NeurIPS 2024Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times
- NeurIPS 2024Neural Experts: Mixture of Experts for Implicit Neural Representations
- NeurIPS 2024NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks
- NeurIPS 2024On Feature Learning in Structured State Space Models
- NeurIPS 2024On Sampling Strategies for Spectral Model Sharding
- NeurIPS 2024Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
- NeurIPS 2024Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
- NeurIPS 2024Parallelizing Linear Transformers with the Delta Rule over Sequence Length
- NeurIPS 2024Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective
- NeurIPS 2024Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
- NeurIPS 2024Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
- NeurIPS 2024PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
- NeurIPS 2024PointMamba: A Simple State Space Model for Point Cloud Analysis
- NeurIPS 2024Poseidon: Efficient Foundation Models for PDEs
- NeurIPS 2024PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond
- NeurIPS 2024Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization
- NeurIPS 2024ProxyFusion: Face Feature Aggregation Through Sparse Experts
- NeurIPS 2024Q-VLM: Post-training Quantization for Large Vision-Language Models
- NeurIPS 2024QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion
- NeurIPS 2024QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
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 查深度学习 · foundation models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-foundation-models"}
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。
「深度学习」下的其他主题
- 扩散模型 6,900
- 深度学习 · generative models and autoencoders 4,314
- 深度学习 · large language models 2,334
- 深度学习 · self supervised learning 1,958
- 深度学习 · graph neural networks 1,648
- 深度学习 · optimization 953
- 深度学习 · representation learning 802
- 深度学习 · probabilistic methods 637
- 深度学习 · causal inference 316
- 深度学习 · transfer learning 197