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

160 papers, page 3 of 4

101. NeurIPS 2024Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
102. NeurIPS 2024Interaction-Force Transport Gradient Flows
103. NeurIPS 2024Inversion-based Latent Bayesian Optimization
104. NeurIPS 2024Is Score Matching Suitable for Estimating Point Processes?
105. NeurIPS 2024Kermut: Composite kernel regression for protein variant effects
106. NeurIPS 2024Learning to be Smooth: An End-to-End Differentiable Particle Smoother
107. NeurIPS 2024Linear Uncertainty Quantification of Graphical Model Inference
108. NeurIPS 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
109. NeurIPS 2024Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk
110. NeurIPS 2024Low Degree Hardness for Broadcasting on Trees
111. NeurIPS 2024Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
112. NeurIPS 2024Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
113. NeurIPS 2024Multivariate Probabilistic Time Series Forecasting with Correlated Errors
114. NeurIPS 2024Mutual Information Estimation via $f$-Divergence and Data Derangements
115. NeurIPS 2024Mutual Information Estimation via Normalizing Flows
116. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
117. NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
118. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
119. NeurIPS 2024On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
120. NeurIPS 2024On the Expressive Power of Tree-Structured Probabilistic Circuits
121. NeurIPS 2024Particle Semi-Implicit Variational Inference
122. NeurIPS 2024Physics-Informed Variational State-Space Gaussian Processes
123. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
124. NeurIPS 2024Practical Shuffle Coding
125. NeurIPS 2024Preferential Normalizing Flows
126. NeurIPS 2024Principled Bayesian Optimization in Collaboration with Human Experts
127. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
128. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
129. NeurIPS 2024Probabilistic size-and-shape functional mixed models
130. NeurIPS 2024Quasi-Bayes meets Vines
131. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
132. NeurIPS 2024Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
133. NeurIPS 2024Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective
134. NeurIPS 2024Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
135. NeurIPS 2024Robust Gaussian Processes via Relevance Pursuit
136. NeurIPS 2024Robust group and simultaneous inferences for high-dimensional single index model
137. NeurIPS 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
138. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
139. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
140. NeurIPS 2024Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
141. NeurIPS 2024Small coresets via negative dependence: DPPs, linear statistics, and concentration
142. NeurIPS 2024Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
143. NeurIPS 2024Sparse Bayesian Generative Modeling for Compressive Sensing
144. NeurIPS 2024Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
145. NeurIPS 2024Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
146. NeurIPS 2024Stopping Bayesian Optimization with Probabilistic Regret Bounds
147. NeurIPS 2024Streaming Bayes GFlowNets
148. NeurIPS 2024Symmetries in Overparametrized Neural Networks: A Mean Field View
149. NeurIPS 2024This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
150. NeurIPS 2024To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty

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.