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 13 of 13

601. NeurIPS 2023Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
602. NeurIPS 2023Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution
603. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
604. NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
605. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
606. NeurIPS 2023Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
607. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
608. NeurIPS 2023On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
609. NeurIPS 2023Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games
610. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
611. NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
612. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
613. NeurIPS 2023Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
614. NeurIPS 2023Probabilistic Exponential Integrators
615. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
616. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
617. NeurIPS 2023Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
618. NeurIPS 2023PROTES: Probabilistic Optimization with Tensor Sampling
619. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
620. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
621. NeurIPS 2023SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
622. NeurIPS 2023SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
623. NeurIPS 2023Semi-Supervised Domain Generalization with Known and Unknown Classes
624. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
625. NeurIPS 2023SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
626. NeurIPS 2023Smooth Flipping Probability for Differential Private Sign Random Projection Methods
627. NeurIPS 2023Soft-Unification in Deep Probabilistic Logic
628. NeurIPS 2023Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
629. NeurIPS 2023Star-Shaped Denoising Diffusion Probabilistic Models
630. NeurIPS 2023Towards Accelerated Model Training via Bayesian Data Selection
631. NeurIPS 2023Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
632. NeurIPS 2023Transformers learn to implement preconditioned gradient descent for in-context learning
633. NeurIPS 2023Uncertainty Quantification via Neural Posterior Principal Components
634. NeurIPS 2023Undirected Probabilistic Model for Tensor Decomposition
635. NeurIPS 2023Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
636. NeurIPS 2023VaRT: Variational Regression Trees
637. 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.