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 11 of 13

501. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
502. NeurIPS 2024Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
503. NeurIPS 2024Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective
504. NeurIPS 2024Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
505. NeurIPS 2024Robust Gaussian Processes via Relevance Pursuit
506. NeurIPS 2024Robust group and simultaneous inferences for high-dimensional single index model
507. NeurIPS 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
508. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
509. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
510. NeurIPS 2024Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
511. NeurIPS 2024Small coresets via negative dependence: DPPs, linear statistics, and concentration
512. NeurIPS 2024Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
513. NeurIPS 2024Sparse Bayesian Generative Modeling for Compressive Sensing
514. NeurIPS 2024Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
515. NeurIPS 2024Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
516. NeurIPS 2024Stopping Bayesian Optimization with Probabilistic Regret Bounds
517. NeurIPS 2024Streaming Bayes GFlowNets
518. NeurIPS 2024Symmetries in Overparametrized Neural Networks: A Mean Field View
519. NeurIPS 2024This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
520. NeurIPS 2024To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty
521. NeurIPS 2024Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy
522. NeurIPS 2024Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees
523. NeurIPS 2024TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing
524. NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
525. NeurIPS 2024Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
526. NeurIPS 2024VISA: Variational Inference with Sequential Sample-Average Approximations
527. NeurIPS 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
528. NeurIPS 2024What type of inference is planning?
529. NeurIPS 2024Why the Metric Backbone Preserves Community Structure
530. NeurIPS 2024Your contrastive learning problem is secretly a distribution alignment problem
531. CVPR 2023Diffusion Probabilistic Model Made Slim
532. CVPR 2023Lookahead Diffusion Probabilistic Models for Refining Mean Estimation
533. CVPR 2023Probabilistic Debiasing of Scene Graphs
534. CVPR 2023Probabilistic Knowledge Distillation of Face Ensembles
535. CVPR 2023Probabilistic Prompt Learning for Dense Prediction
536. CVPR 2023Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
537. ICLR 2023A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
538. ICLR 2023A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
539. ICLR 2023Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
540. ICLR 2023Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models
541. ICLR 2023Diffusion Probabilistic Fields
542. ICLR 2023Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
543. ICLR 2023Learning Probabilistic Topological Representations Using Discrete Morse Theory
544. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
545. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
546. ICML 2023A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
547. ICML 2023Automatically marginalized MCMC in probabilistic programming
548. ICML 2023Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
549. ICML 2023Correcting discount-factor mismatch in on-policy policy gradient methods
550. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

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.