端上学习论文趋势 2023–2026
设备端训练
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 134 篇被归入该主题的论文。
主要结论
- 端上学习论文从 2023 年的 122 篇降到 2026 年的 6 篇,热度在回落。
- NeurIPS 是发端上学习论文最多的会议,2023–2026 年共 91 篇,占四个会议总量的 68%。
- 在「边缘与物联网」的 5 个主题里,它的 2026 年论文数排第 4;同组增长最快的传感融合是 3.8 倍,端上学习是 0.0 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 90 | 0 | 30 | 2 | 122 |
| 2024 | 0 | 0 | 2 | 2 | 4 |
| 2025 | 0 | 1 | 0 | 1 | 2 |
| 2026 | 1 | 0 | 3 | 2 | 6 |
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 2026MobileMoE: Scaling On-Device Mixture of Experts
- ICML 2026Lower Bounds for Frank-Wolfe on Strongly Convex Sets
- CVPR 2026DeDelayed Deleting Remote Inference Delay via On-Device Correction
- ICLR 2025Robust Function-Calling for On-Device Language Model via Function Masking
- CVPR 2025EdgeTAM: On-Device Track Anything Model
- ICML 2024MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
- CVPR 2024Device-Wise Federated Network Pruning
- NeurIPS 2023[Re] Numerical influence of ReLU'(0) on backpropagation
- ICML 2023Adversarial Collaborative Learning on Non-IID Features
- CVPR 2023Efficient On-Device Training via Gradient Filtering
- ICML 2026On the Fragility of Data Attribution When Learning Is Distributed
- CVPR 2026Mobile-VTON High-Fidelity On-Device Virtual Try-On
- ICML 2024On Online Experimentation without Device Identifiers
- CVPR 2024Cloud-Device Collaborative Learning for Multimodal Large Language Models
- NeurIPS 2023[Re] On the Reproducibility of CartoonX
- ICML 2023FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
- CVPR 2023TMO: Textured Mesh Acquisition of Objects With a Mobile Device by Using Differentiable Rendering
- ICML 2026Efficient Preference Poisoning Attack on Offline RLHF
- NeurIPS 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
- ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
全部端上学习论文
共 90 篇,第 1 / 2 页
- NeurIPS 2023[Re] Numerical influence of ReLU'(0) on backpropagation
- NeurIPS 2023[Re] On the Reproducibility of CartoonX
- NeurIPS 2023A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
- NeurIPS 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
- NeurIPS 2023A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
- NeurIPS 2023A Variational Perspective on High-Resolution ODEs
- NeurIPS 2023Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
- NeurIPS 2023Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice
- NeurIPS 2023Adversarial Model for Offline Reinforcement Learning
- NeurIPS 2023ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
- NeurIPS 2023An Empirical Investigation of the Role of Pre-training in Lifelong Learning
- NeurIPS 2023An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
- NeurIPS 2023BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
- NeurIPS 2023Bayesian Optimisation of Functions on Graphs
- NeurIPS 2023Beyond Normal: On the Evaluation of Mutual Information Estimators
- NeurIPS 2023Binary Classification with Confidence Difference
- NeurIPS 2023CLeAR: Continual Learning on Algorithmic Reasoning for Human-like Intelligence
- NeurIPS 2023Collaborative Learning via Prediction Consensus
- NeurIPS 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
- NeurIPS 2023Convolution Monge Mapping Normalization for learning on sleep data
- NeurIPS 2023Cookie Consent Has Disparate Impact on Estimation Accuracy
- NeurIPS 2023Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First
- NeurIPS 2023Doubly-Robust Self-Training
- NeurIPS 2023Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
- NeurIPS 2023EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
- NeurIPS 2023FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
- NeurIPS 2023Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
- NeurIPS 2023HiBug: On Human-Interpretable Model Debug
- NeurIPS 2023Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
- NeurIPS 2023Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
- NeurIPS 2023Large-Scale Distributed Learning via Private On-Device LSH
- NeurIPS 2023Latent SDEs on Homogeneous Spaces
- NeurIPS 2023Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation
- NeurIPS 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
- NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
- NeurIPS 2023Learning to Augment Distributions for Out-of-distribution Detection
- NeurIPS 2023Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
- NeurIPS 2023Learning via Wasserstein-Based High Probability Generalisation Bounds
- NeurIPS 2023Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
- NeurIPS 2023May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
- NeurIPS 2023Mitigating the Effect of Incidental Correlations on Part-based Learning
- NeurIPS 2023Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
- NeurIPS 2023On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
- NeurIPS 2023On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
- NeurIPS 2023On Generalization Bounds for Projective Clustering
- NeurIPS 2023On Imitation in Mean-field Games
- NeurIPS 2023On kernel-based statistical learning theory in the mean field limit
- NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
- NeurIPS 2023On Separate Normalization in Self-supervised Transformers
- NeurIPS 2023On Slicing Optimality for Mutual Information
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 查端上学习在 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": "on-device-learning"}
常见问题
- 2023–2026 年四大会议一共有多少篇端上学习论文?
- 共 134 篇,按年份分别是 2023 年 122 篇、2024 年 4 篇、2025 年 2 篇、2026 年 6 篇。
- NeurIPS 2026 有多少篇端上学习论文?
- NeurIPS 2026 有 1 篇端上学习论文。
- ICLR 2025 有多少篇端上学习论文?
- ICLR 2025 有 1 篇端上学习论文。
- ICML 2026 有多少篇端上学习论文?
- ICML 2026 有 3 篇端上学习论文。
- CVPR 2026 有多少篇端上学习论文?
- CVPR 2026 有 2 篇端上学习论文,上一届是 1 篇。
- 哪个会议的端上学习论文最多?
- NeurIPS,2023–2026 年共 91 篇。