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

35 papers, page 1 of 1

1. ICLR 2026Efficient Autoregressive Inference for Transformer Probabilistic Models
2. ICLR 2026End-to-End Probabilistic Framework for Learning with Hard Constraints
3. ICLR 2026From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
4. ICLR 2026Learning Boltzmann Generators via Constrained Mass Transport
5. ICLR 2026Learning Ordinal Probabilistic Reward from Preferences
6. ICLR 2026Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution
7. ICLR 2026MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
8. ICLR 2026Next-ToBE: Probabilistic Next Token-Bag Exploitation for Activating Anticipatory Capacity in LLMs
9. ICLR 2026Probabilistic Kernel Function for Fast Angle Testing
10. ICLR 2026Reliable Probabilistic Forecasting of Irregular Time Series through Marginalization-Consistent Flows
11. ICLR 2026SafeMPO: Constrained Reinforcement Learning with Probabilistic Incremental Improvement
12. ICLR 2026Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
13. ICLR 2026Uncertainty-driven Embedding Convolution
14. ICLR 2025Efficient Reinforcement Learning with Large Language Model Priors
15. ICLR 2025GPS: A Probabilistic Distributional Similarity with Gumbel Priors for Set-to-Set Matching
16. ICLR 2025Heavy-Tailed Diffusion with Denoising Levy Probabilistic Models
17. ICLR 2025Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
18. ICLR 2025P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS
19. ICLR 2025PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
20. ICLR 2025Probabilistic Conformal Prediction with Approximate Conditional Validity
21. ICLR 2025Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation
22. ICLR 2025The "Law'' of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities
23. ICLR 2025Variational Bayesian Pseudo-Coreset
24. ICLR 2024A Probabilistic Framework for Modular Continual Learning
25. ICLR 2024A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
26. ICLR 2024AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
27. ICLR 2024EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model
28. ICLR 2024Forward $\chi^2$ Divergence Based Variational Importance Sampling
29. ICLR 2023A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
30. ICLR 2023A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
31. ICLR 2023Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
32. ICLR 2023Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models
33. ICLR 2023Diffusion Probabilistic Fields
34. ICLR 2023Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
35. ICLR 2023Learning Probabilistic Topological Representations Using Discrete Morse Theory

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.