深度学习 · probabilistic methods论文趋势 2023–2026

主要结论

• 深度学习 · probabilistic methods论文从 2023 年的 107 篇增长到 2026 年的 107 篇，三年间扩大到约 1.0 倍。
• NeurIPS 是发深度学习 · probabilistic methods论文最多的会议，2023–2026 年共 440 篇，占四个会议总量的 69%。
• 交叉方向里涨得最快的是驾驶仿真：同时属于两个主题的论文从 2023 年的 5 篇增至 2026 年的 14 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 9；同组增长最快的扩散模型是 4.1 倍，深度学习 · probabilistic methods是 1.0 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · probabilistic methods论文

共 212 篇，第 3 / 5 页

101. NeurIPS 2025Large Language Bayes
102. NeurIPS 2025Learning Counterfactual Outcomes Under Rank Preservation
103. NeurIPS 2025Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
104. NeurIPS 2025Learning normalized image densities via dual score matching
105. NeurIPS 2025Learning to Condition: A Neural Heuristic for Scalable MPE Inference
106. NeurIPS 2025Least squares variational inference
107. NeurIPS 2025Less Greedy Equivalence Search
108. NeurIPS 2025Leveraging semantic similarity for experimentation with AI-generated treatments
109. NeurIPS 2025List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression
110. NeurIPS 2025Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
111. NeurIPS 2025Low-Rank Graphon Learning for Networks
112. NeurIPS 2025Martingale Posterior Neural Networks for Fast Sequential Decision Making
113. NeurIPS 2025Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
114. NeurIPS 2025MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
115. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
116. NeurIPS 2025Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
117. NeurIPS 2025MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
118. NeurIPS 2025Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
119. NeurIPS 2025Model-Informed Flows for Bayesian Inference
120. NeurIPS 2025Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs
121. NeurIPS 2025Multi-View Oriented GPLVM: Expressiveness and Efficiency
122. NeurIPS 2025Multilevel neural simulation-based inference
123. NeurIPS 2025Multivariate Latent Recalibration for Conditional Normalizing Flows
124. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
125. NeurIPS 2025Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
126. NeurIPS 2025Neural Hamiltonian Diffusions for Modeling Structured Geometric Dynamics
127. NeurIPS 2025Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
128. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
129. NeurIPS 2025Neurosymbolic Diffusion Models
130. NeurIPS 2025NeuSymEA: Neuro-symbolic Entity Alignment via Variational Inference
131. NeurIPS 2025Non-equilibrium Annealed Adjoint Sampler
132. NeurIPS 2025Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shift with Unlabeled Data
133. NeurIPS 2025Non-Stationary Structural Causal Bandits
134. NeurIPS 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
135. NeurIPS 2025On scalable and efficient training of diffusion samplers
136. NeurIPS 2025On the Hardness of Approximating Distributions with Tractable Probabilistic Models
137. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
138. NeurIPS 2025One Sample is Enough to Make Conformal Prediction Robust
139. NeurIPS 2025Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
140. NeurIPS 2025Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
141. NeurIPS 2025Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
142. NeurIPS 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
143. NeurIPS 2025Parallelizing MCMC Across the Sequence Length
144. NeurIPS 2025Personalized Federated Conformal Prediction with Localization
145. NeurIPS 2025PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
146. NeurIPS 2025Practical Kernel Selection for Kernel-based Conditional Independence Test
147. NeurIPS 2025Prediction-Powered Causal Inferences
148. NeurIPS 2025Probabilistic Token Alignment for Large Language Model Fusion
149. NeurIPS 2025ProDAG: Projected Variational Inference for Directed Acyclic Graphs
150. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · probabilistic methods论文？

共 637 篇，按年份分别是 2023 年 107 篇、2024 年 160 篇、2025 年 263 篇、2026 年 107 篇。

NeurIPS 2026 有多少篇深度学习 · probabilistic methods论文？

NeurIPS 2026 有 28 篇深度学习 · probabilistic methods论文，上一届是 212 篇。

ICLR 2026 有多少篇深度学习 · probabilistic methods论文？

ICLR 2026 有 13 篇深度学习 · probabilistic methods论文，上一届是 10 篇。

ICML 2026 有多少篇深度学习 · probabilistic methods论文？

ICML 2026 有 59 篇深度学习 · probabilistic methods论文，上一届是 37 篇。

CVPR 2026 有多少篇深度学习 · probabilistic methods论文？

CVPR 2026 有 7 篇深度学习 · probabilistic methods论文，上一届是 4 篇。

哪个会议的深度学习 · probabilistic methods论文最多？

NeurIPS，2023–2026 年共 440 篇。