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

160 papers, page 1 of 4

1. CVPR 2024A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
2. CVPR 2024CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
3. CVPR 2024DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans
4. CVPR 2024Observation-Guided Diffusion Probabilistic Models
5. CVPR 2024Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
6. CVPR 2024ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring
7. ICLR 2024A Probabilistic Framework for Modular Continual Learning
8. ICLR 2024A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
9. ICLR 2024AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
10. ICLR 2024EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model
11. ICLR 2024Forward $\chi^2$ Divergence Based Variational Importance Sampling
12. ICML 2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
13. ICML 2024CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
14. ICML 2024Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
15. ICML 2024Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
16. ICML 2024Efficient Denoising Diffusion via Probabilistic Masking
17. ICML 2024Explaining Probabilistic Models with Distributional Values
18. ICML 2024Incorporating probabilistic domain knowledge into deep multiple instance learning
19. ICML 2024On the Hardness of Probabilistic Neurosymbolic Learning
20. ICML 2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
21. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
22. ICML 2024Probabilistic Generating Circuits - Demystified
23. ICML 2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
24. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
25. ICML 2024Robust Inverse Graphics via Probabilistic Inference
26. ICML 2024Scaling Tractable Probabilistic Circuits: A Systems Perspective
27. ICML 2024SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
28. ICML 2024Total Variation Distance Meets Probabilistic Inference
29. ICML 2024Trust Regions for Explanations via Black-Box Probabilistic Certification
30. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
31. NeurIPS 2024A Compositional Atlas for Algebraic Circuits
32. NeurIPS 2024A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics
33. NeurIPS 2024A Metalearned Neural Circuit for Nonparametric Bayesian Inference
34. NeurIPS 2024A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models
35. NeurIPS 2024A Theoretical Perspective for Speculative Decoding Algorithm
36. NeurIPS 2024A Tractable Inference Perspective of Offline RL
37. NeurIPS 2024Abductive Reasoning in Logical Credal Networks
38. NeurIPS 2024Accelerating Relative Entropy Coding with Space Partitioning
39. NeurIPS 2024Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
40. NeurIPS 2024Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
41. NeurIPS 2024Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence
42. NeurIPS 2024Amortized Bayesian Experimental Design for Decision-Making
43. NeurIPS 2024Amortizing intractable inference in diffusion models for vision, language, and control
44. NeurIPS 2024An Analysis of Elo Rating Systems via Markov Chains
45. NeurIPS 2024An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
46. NeurIPS 2024An Information Theoretic Perspective on Conformal Prediction
47. NeurIPS 2024Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
48. NeurIPS 2024Approximately Equivariant Neural Processes
49. NeurIPS 2024Approximation-Aware Bayesian Optimization
50. NeurIPS 2024Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families

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.