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

107 papers, page 2 of 3

51. NeurIPS 2023Credal Marginal MAP
52. NeurIPS 2023Deep Fractional Fourier Transform
53. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
54. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
55. NeurIPS 2023Deep Recurrent Optimal Stopping
56. NeurIPS 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
57. NeurIPS 2023Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
58. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
59. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
60. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
61. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
62. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
63. NeurIPS 2023How many samples are needed to leverage smoothness?
64. NeurIPS 2023Human spatiotemporal pattern learning as probabilistic program synthesis
65. NeurIPS 2023Imitation Learning from Vague Feedback
66. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
67. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
68. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
69. NeurIPS 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
70. NeurIPS 2023Learning Functional Transduction
71. NeurIPS 2023Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
72. NeurIPS 2023Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution
73. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
74. NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
75. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
76. NeurIPS 2023Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
77. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
78. NeurIPS 2023On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
79. NeurIPS 2023Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games
80. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
81. NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
82. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
83. NeurIPS 2023Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
84. NeurIPS 2023Probabilistic Exponential Integrators
85. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
86. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
87. NeurIPS 2023Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
88. NeurIPS 2023PROTES: Probabilistic Optimization with Tensor Sampling
89. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
90. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
91. NeurIPS 2023SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
92. NeurIPS 2023SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
93. NeurIPS 2023Semi-Supervised Domain Generalization with Known and Unknown Classes
94. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
95. NeurIPS 2023SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
96. NeurIPS 2023Smooth Flipping Probability for Differential Private Sign Random Projection Methods
97. NeurIPS 2023Soft-Unification in Deep Probabilistic Logic
98. NeurIPS 2023Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
99. NeurIPS 2023Star-Shaped Denoising Diffusion Probabilistic Models
100. NeurIPS 2023Towards Accelerated Model Training via Bayesian Data Selection

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.