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

69 papers, page 2 of 2

51. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
52. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
53. NeurIPS 2023SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
54. NeurIPS 2023SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
55. NeurIPS 2023Semi-Supervised Domain Generalization with Known and Unknown Classes
56. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
57. NeurIPS 2023SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
58. NeurIPS 2023Smooth Flipping Probability for Differential Private Sign Random Projection Methods
59. NeurIPS 2023Soft-Unification in Deep Probabilistic Logic
60. NeurIPS 2023Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
61. NeurIPS 2023Star-Shaped Denoising Diffusion Probabilistic Models
62. NeurIPS 2023Towards Accelerated Model Training via Bayesian Data Selection
63. NeurIPS 2023Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
64. NeurIPS 2023Transformers learn to implement preconditioned gradient descent for in-context learning
65. NeurIPS 2023Uncertainty Quantification via Neural Posterior Principal Components
66. NeurIPS 2023Undirected Probabilistic Model for Tensor Decomposition
67. NeurIPS 2023Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
68. NeurIPS 2023VaRT: Variational Regression Trees
69. NeurIPS 2023VeriX: Towards Verified Explainability of Deep Neural Networks

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.