Deep learning · probabilistic methods papers, 2023–2026

Key findings

• Deep learning · probabilistic methods went from 107 papers in 2023 to 107 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Deep learning · probabilistic methods work: 440 papers from 2023 to 2026, 69% of the four-venue total.
• The fastest-growing overlap is with Simulation for Driving: papers tagged with both rose from 5 in 2023 to 14 in 2026.
• Among the 11 topics under Deep Learning, it ranks #9 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
• ICML 2026An Efficient Joint Learning Approach for Item Response Theory
• ICLR 2026Probabilistic Kernel Function for Fast Angle Testing
• CVPR 2026Beyond Text Visual Description Assembly by Probabilistic Model for CLIP-based
• NeurIPS 2025$\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
• ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
• ICLR 2025P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS
• CVPR 2025ProbPose: A Probabilistic Approach to 2D Human Pose Estimation
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
• ICLR 2024A Probabilistic Framework for Modular Continual Learning
• CVPR 2024CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
• NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
• ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
• ICLR 2023A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
• CVPR 2023Probabilistic Knowledge Distillation of Face Ensembles
• NeurIPS 2026Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
• ICML 2026Avoid What You Know: Divergent Trajectory Balance for GFlowNets
• ICLR 2026Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution
• CVPR 2026ARES Unifying Asymmetric RGB-Event Stereo for Probabilistic Scene Flow Estimation

All Deep learning · probabilistic methods papers

263 papers, page 1 of 6

1. CVPR 2025Fingerprinting Denoising Diffusion Probabilistic Models
2. CVPR 2025Probabilistic Prompt Distribution Learning for Animal Pose Estimation
3. CVPR 2025ProbPose: A Probabilistic Approach to 2D Human Pose Estimation
4. CVPR 2025SyncSDE: A Probabilistic Framework for Diffusion Synchronization
5. ICLR 2025Efficient Reinforcement Learning with Large Language Model Priors
6. ICLR 2025GPS: A Probabilistic Distributional Similarity with Gumbel Priors for Set-to-Set Matching
7. ICLR 2025Heavy-Tailed Diffusion with Denoising Levy Probabilistic Models
8. ICLR 2025Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
9. ICLR 2025P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS
10. ICLR 2025PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
11. ICLR 2025Probabilistic Conformal Prediction with Approximate Conditional Validity
12. ICLR 2025Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation
13. ICLR 2025The "Law'' of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities
14. ICLR 2025Variational Bayesian Pseudo-Coreset
15. ICML 2025A Mixture-Based Framework for Guiding Diffusion Models
16. ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
17. ICML 2025Calibrated Value-Aware Model Learning with Probabilistic Environment Models
18. ICML 2025Can Transformers Learn Full Bayesian Inference in Context?
19. ICML 2025Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
20. ICML 2025Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention
21. ICML 2025Density Ratio Estimation with Conditional Probability Paths
22. ICML 2025Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions
23. ICML 2025Doubly Robust Conformalized Survival Analysis with Right-Censored Data
24. ICML 2025Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity
25. ICML 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
26. ICML 2025Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
27. ICML 2025Gridded Transformer Neural Processes for Spatio-Temporal Data
28. ICML 2025Improving the Statistical Efficiency of Cross-Conformal Prediction
29. ICML 2025LEAPS: A discrete neural sampler via locally equivariant networks
30. ICML 2025Learning multivariate Gaussians with imperfect advice
31. ICML 2025Learning Survival Distributions with the Asymmetric Laplace Distribution
32. ICML 2025Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints
33. ICML 2025Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules
34. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
35. ICML 2025Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
36. ICML 2025Prediction-Powered E-Values
37. ICML 2025Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
38. ICML 2025Random Policy Evaluation Uncovers Policies of Generative Flow Networks
39. ICML 2025Rethinking Aleatoric and Epistemic Uncertainty
40. ICML 2025Robust Conformal Outlier Detection under Contaminated Reference Data
41. ICML 2025Sample Complexity of Branch-length Estimation by Maximum Likelihood
42. ICML 2025Scaling Probabilistic Circuits via Monarch Matrices
43. ICML 2025Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
44. ICML 2025The Limits of Tractable Marginalization
45. ICML 2025To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
46. ICML 2025TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
47. ICML 2025Trajectory Inference with Smooth Schrödinger Bridges
48. ICML 2025Variational Learning of Fractional Posteriors
49. ICML 2025Variational Phylogenetic Inference with Products over Bipartitions
50. ICML 2025Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

FAQ

How many Deep learning · probabilistic methods papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

637 in total: 107 in 2023, 160 in 2024, 263 in 2025, 107 in 2026.

How many Deep learning · probabilistic methods papers are at NeurIPS 2026?

NeurIPS 2026 has 28 Deep learning · probabilistic methods papers, versus 212 the year before.

How many Deep learning · probabilistic methods papers are at ICLR 2026?

ICLR 2026 has 13 Deep learning · probabilistic methods papers, versus 10 the year before.

How many Deep learning · probabilistic methods papers are at ICML 2026?

ICML 2026 has 59 Deep learning · probabilistic methods papers, versus 37 the year before.

How many Deep learning · probabilistic methods papers are at CVPR 2026?

CVPR 2026 has 7 Deep learning · probabilistic methods papers, versus 4 the year before.

Which conference publishes the most Deep learning · probabilistic methods papers?

NeurIPS, with 440 papers from 2023 to 2026.