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

107 papers, page 1 of 3

1. CVPR 2023Diffusion Probabilistic Model Made Slim
2. CVPR 2023Lookahead Diffusion Probabilistic Models for Refining Mean Estimation
3. CVPR 2023Probabilistic Debiasing of Scene Graphs
4. CVPR 2023Probabilistic Knowledge Distillation of Face Ensembles
5. CVPR 2023Probabilistic Prompt Learning for Dense Prediction
6. CVPR 2023Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
7. ICLR 2023A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
8. ICLR 2023A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
9. ICLR 2023Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
10. ICLR 2023Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models
11. ICLR 2023Diffusion Probabilistic Fields
12. ICLR 2023Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
13. ICLR 2023Learning Probabilistic Topological Representations Using Discrete Morse Theory
14. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
15. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
16. ICML 2023A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
17. ICML 2023Automatically marginalized MCMC in probabilistic programming
18. ICML 2023Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
19. ICML 2023Correcting discount-factor mismatch in on-policy policy gradient methods
20. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
21. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
22. ICML 2023FARE: Provably Fair Representation Learning with Practical Certificates
23. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
24. ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
25. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
26. ICML 2023Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
27. ICML 2023OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
28. ICML 2023On the Convergence Rates of Policy Gradient Methods
29. ICML 2023Principled Acceleration of Iterative Numerical Methods Using Machine Learning
30. ICML 2023Probabilistic Attention-to-Influence Neural Models for Event Sequences
31. ICML 2023Probabilistic Categorical Adversarial Attack and Adversarial Training
32. ICML 2023Probabilistic Concept Bottleneck Models
33. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
34. ICML 2023Probabilistic Imputation for Time-series Classification with Missing Data
35. ICML 2023Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
36. ICML 2023Quantile Credit Assignment
37. ICML 2023Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
38. ICML 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
39. NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
40. NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
41. NeurIPS 2023A Heavy-Tailed Algebra for Probabilistic Programming
42. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
43. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
44. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
45. NeurIPS 2023Block Broyden's Methods for Solving Nonlinear Equations
46. NeurIPS 2023Byzantine-Tolerant Methods for Distributed Variational Inequalities
47. NeurIPS 2023Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
48. NeurIPS 2023Characteristic Circuits
49. NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
50. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation

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.