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

440 papers, page 7 of 9

301. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
302. NeurIPS 2024Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
303. NeurIPS 2024Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
304. NeurIPS 2024High Rank Path Development: an approach to learning the filtration of stochastic processes
305. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
306. NeurIPS 2024Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection
307. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
308. NeurIPS 2024Improved off-policy training of diffusion samplers
309. NeurIPS 2024Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
310. NeurIPS 2024In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
311. NeurIPS 2024Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
312. NeurIPS 2024Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
313. NeurIPS 2024Interaction-Force Transport Gradient Flows
314. NeurIPS 2024Inversion-based Latent Bayesian Optimization
315. NeurIPS 2024Is Score Matching Suitable for Estimating Point Processes?
316. NeurIPS 2024Kermut: Composite kernel regression for protein variant effects
317. NeurIPS 2024Learning to be Smooth: An End-to-End Differentiable Particle Smoother
318. NeurIPS 2024Linear Uncertainty Quantification of Graphical Model Inference
319. NeurIPS 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
320. NeurIPS 2024Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk
321. NeurIPS 2024Low Degree Hardness for Broadcasting on Trees
322. NeurIPS 2024Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
323. NeurIPS 2024Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
324. NeurIPS 2024Multivariate Probabilistic Time Series Forecasting with Correlated Errors
325. NeurIPS 2024Mutual Information Estimation via $f$-Divergence and Data Derangements
326. NeurIPS 2024Mutual Information Estimation via Normalizing Flows
327. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
328. NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
329. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
330. NeurIPS 2024On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
331. NeurIPS 2024On the Expressive Power of Tree-Structured Probabilistic Circuits
332. NeurIPS 2024Particle Semi-Implicit Variational Inference
333. NeurIPS 2024Physics-Informed Variational State-Space Gaussian Processes
334. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
335. NeurIPS 2024Practical Shuffle Coding
336. NeurIPS 2024Preferential Normalizing Flows
337. NeurIPS 2024Principled Bayesian Optimization in Collaboration with Human Experts
338. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
339. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
340. NeurIPS 2024Probabilistic size-and-shape functional mixed models
341. NeurIPS 2024Quasi-Bayes meets Vines
342. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
343. NeurIPS 2024Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
344. NeurIPS 2024Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective
345. NeurIPS 2024Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
346. NeurIPS 2024Robust Gaussian Processes via Relevance Pursuit
347. NeurIPS 2024Robust group and simultaneous inferences for high-dimensional single index model
348. NeurIPS 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
349. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
350. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood

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.