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

28 papers, page 1 of 1

1. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
2. NeurIPS 2026A probabilistic model of visual segmentation explains early visual cortical dynamics
3. NeurIPS 2026Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
4. NeurIPS 2026Announced Breaks Separate Conformal Reliability from Frequency Calibration
5. NeurIPS 2026Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift
6. NeurIPS 2026CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language Models
7. NeurIPS 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representations
8. NeurIPS 2026Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
9. NeurIPS 2026Filtered-Trace Online Variational Training for Probabilistic Spiking Neural Networks
10. NeurIPS 2026FlowBatt: Flow Matching for Probabilistic Battery Degradation Prediction
11. NeurIPS 2026Heterogeneous Judge-Aware Ranking with Sensitivity, Disagreement, and Confidence
12. NeurIPS 2026Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
13. NeurIPS 2026Inference and estimation with unidentifiable latent treatment effects
14. NeurIPS 2026Just Ramp-Up: Debiasing Regression-based Estimator for A/B Tests under Network Interference
15. NeurIPS 2026One-Shot Generative Flows: Existence and Obstructions
16. NeurIPS 2026Probabilistic Recursive Reasoning
17. NeurIPS 2026Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures
18. NeurIPS 2026Probabilistic Tiny Recursive Model
19. NeurIPS 2026probly: Uncertainty-Aware Machine Learning
20. NeurIPS 2026Robust Conditional Conformal Prediction via Branched Normalizing Flow
21. NeurIPS 2026Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
22. NeurIPS 2026Score-based Variational Inference via Quantum Maximally Mixed States
23. NeurIPS 2026Sliced Inner Product Gromov–Wasserstein Distances
24. NeurIPS 2026Stabilizing Few-Shot Object Detection with Language-Conditioned Probabilistic Prototypes
25. NeurIPS 2026Stable GFlowNets with Probabilistic Guarantees
26. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
27. NeurIPS 2026Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
28. NeurIPS 2026Where to Approximate in Neurosymbolic Inference?

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.