Inference Acceleration papers, 2023–2026

Key findings

• Inference Acceleration went from 54 papers in 2023 to 91 in 2026, roughly 1.7x in three years.
• NeurIPS publishes the most Inference Acceleration work: 90 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Deep learning · large language models: papers tagged with both rose from 1 in 2023 to 9 in 2026.
• Among the 5 topics under Efficiency and Systems, it ranks #5 by 2026 paper count; the fastest grower in the group, Hardware-Aware ML, is up 4.4x versus 1.7x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Method-Class Divide in Sub-4-Bit Quantization:\\Iterative vs.\ Single-Pass Sensitivity to Data Composition
• ICML 2026Consistency Deep Equilibrium Models
• ICLR 2026OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
• CVPR 2026GeoRK2 Geometry-Guided Runge-Kutta Integration for Diffusion Transformer Acceleration
• NeurIPS 2025PolarQuant: Leveraging Polar Transformation for Key Cache Quantization and Decoding Acceleration
• ICML 2025RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
• ICLR 2025Progressive distillation induces an implicit curriculum
• CVPR 2025Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
• NeurIPS 2024Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
• ICML 2024Diversified Batch Selection for Training Acceleration
• ICLR 2024Embarrassingly Simple Dataset Distillation
• CVPR 2024Data-Free Quantization via Pseudo-label Filtering
• NeurIPS 2023Continuous-time Analysis of Anchor Acceleration
• ICML 2023Auxiliary Modality Learning with Generalized Curriculum Distillation
• ICLR 2023Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
• CVPR 2023NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization
• NeurIPS 2026AcceleGrad#: Adaptive Geometry-Aware Acceleration
• ICML 2026PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
• ICLR 2026NLI : Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
• CVPR 2026SODA Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer

All Inference Acceleration papers

54 papers, page 1 of 2

1. CVPR 2023Adaptive Data-Free Quantization
2. CVPR 2023Binary Latent Diffusion
3. CVPR 2023Boost Vision Transformer With GPU-Friendly Sparsity and Quantization
4. CVPR 2023DepGraph: Towards Any Structural Pruning
5. CVPR 2023DistilPose: Tokenized Pose Regression With Heatmap Distillation
6. CVPR 2023DisWOT: Student Architecture Search for Distillation WithOut Training
7. CVPR 2023Efficient RGB-T Tracking via Cross-Modality Distillation
8. CVPR 2023Generic-to-Specific Distillation of Masked Autoencoders
9. CVPR 2023Hard Sample Matters a Lot in Zero-Shot Quantization
10. CVPR 2023Network Expansion for Practical Training Acceleration
11. CVPR 2023NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization
12. CVPR 2023On Distillation of Guided Diffusion Models
13. CVPR 2023PD-Quant: Post-Training Quantization Based on Prediction Difference Metric
14. CVPR 2023Post-Training Quantization on Diffusion Models
15. CVPR 2023Practical Network Acceleration With Tiny Sets
16. CVPR 2023Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
17. ICLR 2023Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
18. ICLR 2023Bit-Pruning: A Sparse Multiplication-Less Dot-Product
19. ICLR 2023Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization
20. ICLR 2023EVC: Towards Real-Time Neural Image Compression with Mask Decay
21. ICLR 2023Masked Distillation with Receptive Tokens
22. ICLR 2023Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
23. ICLR 2023MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
24. ICLR 2023On the Convergence of AdaGrad(Norm) on $\mathbb{R}^d$: Beyond Convexity, Non-Asymptotic Rate and Acceleration
25. ICLR 2023PowerQuant: Automorphism Search for Non-Uniform Quantization
26. ICLR 2023Scaffolding a Student to Instill Knowledge
27. ICLR 2023Win: Weight-Decay-Integrated Nesterov Acceleration for Adaptive Gradient Algorithms
28. ICML 2023Anti-Exploration by Random Network Distillation
29. ICML 2023Auxiliary Modality Learning with Generalized Curriculum Distillation
30. ICML 2023Effective and Efficient Structural Inference with Reservoir Computing
31. ICML 2023Fast Inference from Transformers via Speculative Decoding
32. ICML 2023Fast Private Kernel Density Estimation via Locality Sensitive Quantization
33. ICML 2023Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
34. ICML 2023MixFlows: principled variational inference via mixed flows
35. NeurIPS 2023Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
36. NeurIPS 2023Conformalized matrix completion
37. NeurIPS 2023Continuous-time Analysis of Anchor Acceleration
38. NeurIPS 2023Counterfactually Comparing Abstaining Classifiers
39. NeurIPS 2023Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
40. NeurIPS 2023Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee
41. NeurIPS 2023First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
42. NeurIPS 2023Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
43. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
44. NeurIPS 2023Knowledge Distillation for High Dimensional Search Index
45. NeurIPS 2023Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
46. NeurIPS 2023Pseudo-Likelihood Inference
47. NeurIPS 2023QuIP: 2-Bit Quantization of Large Language Models With Guarantees
48. NeurIPS 2023Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
49. NeurIPS 2023Speculative Decoding with Big Little Decoder
50. NeurIPS 2023Students Parrot Their Teachers: Membership Inference on Model Distillation

FAQ

How many Inference Acceleration papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

259 in total: 54 in 2023, 50 in 2024, 64 in 2025, 91 in 2026.

How many Inference Acceleration papers are at NeurIPS 2026?

NeurIPS 2026 has 33 Inference Acceleration papers, versus 15 the year before.

How many Inference Acceleration papers are at ICLR 2026?

ICLR 2026 has 17 Inference Acceleration papers, versus 18 the year before.

How many Inference Acceleration papers are at ICML 2026?

ICML 2026 has 26 Inference Acceleration papers, versus 22 the year before.

How many Inference Acceleration papers are at CVPR 2026?

CVPR 2026 has 15 Inference Acceleration papers, versus 9 the year before.

Which conference publishes the most Inference Acceleration papers?

NeurIPS, with 90 papers from 2023 to 2026.