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

637 papers, page 8 of 13

351. NeurIPS 2025Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
352. NeurIPS 2025Transferring Causal Effects using Proxies
353. NeurIPS 2025Treatment Effect Estimation for Optimal Decision-Making
354. NeurIPS 2025Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
355. NeurIPS 2025Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
356. NeurIPS 2025Uncertainty Quantification with the Empirical Neural Tangent Kernel
357. NeurIPS 2025Universal Causal Inference in a Topos
358. NeurIPS 2025Unveiling Extraneous Sampling Bias with Data Missing-Not-At-Random
359. NeurIPS 2025Variance-Reduced Long-Term Rehearsal Learning with Quadratic Programming Reformulation
360. NeurIPS 2025Variational Inference with Mixtures of Isotropic Gaussians
361. NeurIPS 2025Variational Polya Tree
362. NeurIPS 2025Variational Uncertainty Decomposition for In-Context Learning
363. NeurIPS 2025VERA: Variational Inference Framework for Jailbreaking Large Language Models
364. NeurIPS 2025VIKING: Deep variational inference with stochastic projections
365. NeurIPS 2025Wasserstein Convergence of Critically Damped Langevin Diffusions
366. NeurIPS 2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
367. NeurIPS 2025When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
368. NeurIPS 2025When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
369. NeurIPS 2025Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
370. NeurIPS 2025Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
371. CVPR 2024A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
372. CVPR 2024CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
373. CVPR 2024DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans
374. CVPR 2024Observation-Guided Diffusion Probabilistic Models
375. CVPR 2024Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
376. CVPR 2024ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring
377. ICLR 2024A Probabilistic Framework for Modular Continual Learning
378. ICLR 2024A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
379. ICLR 2024AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
380. ICLR 2024EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model
381. ICLR 2024Forward $\chi^2$ Divergence Based Variational Importance Sampling
382. ICML 2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
383. ICML 2024CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
384. ICML 2024Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
385. ICML 2024Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
386. ICML 2024Efficient Denoising Diffusion via Probabilistic Masking
387. ICML 2024Explaining Probabilistic Models with Distributional Values
388. ICML 2024Incorporating probabilistic domain knowledge into deep multiple instance learning
389. ICML 2024On the Hardness of Probabilistic Neurosymbolic Learning
390. ICML 2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
391. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
392. ICML 2024Probabilistic Generating Circuits - Demystified
393. ICML 2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
394. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
395. ICML 2024Robust Inverse Graphics via Probabilistic Inference
396. ICML 2024Scaling Tractable Probabilistic Circuits: A Systems Perspective
397. ICML 2024SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
398. ICML 2024Total Variation Distance Meets Probabilistic Inference
399. ICML 2024Trust Regions for Explanations via Black-Box Probabilistic Certification
400. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph 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.