稀疏加速论文趋势 2023–2026
稀疏算子
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 260 篇被归入该主题的论文。
主要结论
- 稀疏加速论文从 2023 年的 54 篇增长到 2026 年的 89 篇，三年间扩大到约 1.6 倍。
- NeurIPS 是发稀疏加速论文最多的会议，2023–2026 年共 80 篇，占四个会议总量的 31%。
- 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 13 篇。
- 在「硬件与编译」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的端侧推理是 4.0 倍，稀疏加速是 1.6 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 10 | 22 | 11 | 11 | 54 |
| 2024 | 20 | 10 | 18 | 5 | 53 |
| 2025 | 21 | 16 | 22 | 5 | 64 |
| 2026 | 29 | 24 | 24 | 12 | 89 |
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 2026AC/DC on a Budget -- Alternating Sparse Phases
- ICML 2026An Evidential Route to Asymptotic Bayes Optimality under Sparsity
- ICLR 2026Hilbert-Guided Sparse Local Attention
- CVPR 2026Improving Sparse Autoencoder with Dynamic Attention
- NeurIPS 2025Bilevel Network Learning via Hierarchically Structured Sparsity
- ICML 2025COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
- ICLR 2025Beyond Worst-Case Dimensionality Reduction for Sparse Vectors
- CVPR 2025ICP: Immediate Compensation Pruning for Mid-to-high Sparsity
- NeurIPS 2024ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
- ICML 2024Accelerating Transformer Pre-training with 2:4 Sparsity
- ICLR 2024Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
- CVPR 2024Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
- NeurIPS 2023Dynamic Sparsity Is Channel-Level Sparsity Learner
- ICML 2023Conformalization of Sparse Generalized Linear Models
- ICLR 2023Bit-Pruning: A Sparse Multiplication-Less Dot-Product
- CVPR 2023Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
- NeurIPS 2026Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU
- ICML 2026FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
- ICLR 2026Identifiability Challenges in Sparse Linear Ordinary Differential Equations
- CVPR 2026Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction
全部稀疏加速论文
共 24 篇，第 1 / 1 页
- ICML 2026AdaSplash-2: Faster Differentiable Sparse Attention
- ICML 2026An Evidential Route to Asymptotic Bayes Optimality under Sparsity
- ICML 2026AsyncSpade: Efficient Test-Time Scaling with Asynchronous Sparse Decoding
- ICML 2026Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering
- ICML 2026Column Thresholding for Sparse Spiked Wigner Models: Improved Signal Strength Requirements
- ICML 2026DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding
- ICML 2026Elastic Attention: Test-time Adaptive Sparsity Ratios for Efficient Transformers
- ICML 2026Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding
- ICML 2026FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
- ICML 2026HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces
- ICML 2026Incremental Learning of Sparse Attention Patterns in Transformers
- ICML 2026Lie-Algebraic Acceleration of Neural Koopman Dynamics
- ICML 2026MACKO: Sparse matrix-vector multiplication for low sparsity
- ICML 2026Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling
- ICML 2026Narrowing the ANN–SNN Gap for Continuous 1D Temporal Signal Classification with Multi-Scale Temporal Encoding and Sparsity-Regularized Transform Encoding
- ICML 2026Prism: Spectral-Aware Block-Sparse Attention
- ICML 2026RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformers
- ICML 2026RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models
- ICML 2026Size Transferability of Graph Convolutional Networks across Sparsity: A Generalized Graphon Perspective
- ICML 2026SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
- ICML 2026SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training
- ICML 2026Sparser Block-Sparse Attention via Token Permutation
- ICML 2026Weight-sparse transformers have interpretable circuits
- ICML 2026When Does Sparsity Mitigate the Curse of Depth in LLMs
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 查稀疏加速在 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": "sparsity-acceleration"}
常见问题
- 2023–2026 年四大会议一共有多少篇稀疏加速论文？
- 共 260 篇，按年份分别是 2023 年 54 篇、2024 年 53 篇、2025 年 64 篇、2026 年 89 篇。
- NeurIPS 2026 有多少篇稀疏加速论文？
- NeurIPS 2026 有 29 篇稀疏加速论文，上一届是 21 篇。
- ICLR 2026 有多少篇稀疏加速论文？
- ICLR 2026 有 24 篇稀疏加速论文，上一届是 16 篇。
- ICML 2026 有多少篇稀疏加速论文？
- ICML 2026 有 24 篇稀疏加速论文，上一届是 22 篇。
- CVPR 2026 有多少篇稀疏加速论文？
- CVPR 2026 有 12 篇稀疏加速论文，上一届是 5 篇。
- 哪个会议的稀疏加速论文最多？
- NeurIPS，2023–2026 年共 80 篇。