Deep learning · generative models and autoencoders papers, 2023–2026

Key findings

• Deep learning · generative models and autoencoders went from 1,216 papers in 2023 to 1,430 in 2026, roughly 1.2x in three years.
• NeurIPS publishes the most Deep learning · generative models and autoencoders work: 1,714 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Image Generation: papers tagged with both rose from 35 in 2023 to 150 in 2026.
• Among the 11 topics under Deep Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
• ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
• ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
• CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
• NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
• ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
• ICLR 2025A Watermark for Order-Agnostic Language Models
• CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Fixed-Point Approach for Causal Generative Modeling
• ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
• ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
• CVPR 20233D GAN Inversion With Facial Symmetry Prior
• NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
• ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
• ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
• CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back

All Deep learning · generative models and autoencoders papers

4,314 papers, page 27 of 87

1301. NeurIPS 2026Phase Kernel Lifts Capacity of Dense Associative Memory
1302. NeurIPS 2026PhaseDance: Capturing Rhythm and Expressivity in Dance Modeling
1303. NeurIPS 2026PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
1304. NeurIPS 2026Plan4D: Generative Plannable 4D Worlds
1305. NeurIPS 2026Plug-and-Play ADMM for Inverse Problems with Flow Matching Denoiser
1306. NeurIPS 2026Point-to-Manifold Geometry: Flexibly Overcoming the Curse of Dimensionality in Neural Computational Units
1307. NeurIPS 2026Position: We Need Greater Transparency to Maintain Research Pipeline Reliability Despite GenAI
1308. NeurIPS 2026Positional Encoding and Prompt-Order Sensitivity in In-Context Learning
1309. NeurIPS 2026PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
1310. NeurIPS 2026PP-Mark: Provable and Publicly Verifiable Watermarking for Generative AI
1311. NeurIPS 2026Preconditioned Flow Matching
1312. NeurIPS 2026Predict-Project-Renoise: Sampling Diffusion Models under Hard Constraints
1313. NeurIPS 2026Predicting directional flexibility in proteins
1314. NeurIPS 2026Prevailing Bisimulation Metric Learning Is Biased: Implicit Regularization and Its Remedy
1315. NeurIPS 2026PRISM:Disentangling Preference Distributions for Generative Ranking
1316. NeurIPS 2026Probability-Conserving Flow Guidance
1317. NeurIPS 2026ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
1318. NeurIPS 2026Projection Learning: A Principled Way to Overcome Memorization in Distribution Learning
1319. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
1320. NeurIPS 2026RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance
1321. NeurIPS 2026RA-ClipScore: Making Generative Model Evaluation More Interpretable
1322. NeurIPS 2026RadarMAE: Injecting Physical Inductive Biases into Masked Autoencoders for Advanced Radar Object Detection
1323. NeurIPS 2026Rate-Constrained Edge Metadata for Sender–Receiver Generative Video Super-Resolution
1324. NeurIPS 2026Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
1325. NeurIPS 2026Reasoning with Sampling: Cutting at Decision Points
1326. NeurIPS 2026Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
1327. NeurIPS 2026Reflected Schrödinger Bridge Matching
1328. NeurIPS 2026Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow Models
1329. NeurIPS 2026Relaxation-Aligned State Control for Test-Time Scaling in Generative Combinatorial Optimization
1330. NeurIPS 2026Renoise Consistency: Unlocking Efficient Self-Correction for Diffusion Large Language Models
1331. NeurIPS 2026Rethinking Knowledge Distillation for Diffusion Language Models
1332. NeurIPS 2026Rethinking Memorization–Generalization Trade-Off in Generative Models
1333. NeurIPS 2026Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy
1334. NeurIPS 2026Rethinking Semantic ID Construction for Generative Recommendation: SimHash with Parallel Decoding and Semantic Alignment
1335. NeurIPS 2026Rethinking Vector Field Learning for Generative Segmentation
1336. NeurIPS 2026Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review
1337. NeurIPS 2026Revisiting The Power of Closed-Form: Robust Deep Image Prototype Discovery via Scale Mixtures
1338. NeurIPS 2026Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Cases
1339. NeurIPS 2026Reward Is Not a Universal Interface for Generative Reinforcement Learning
1340. NeurIPS 2026Reweighted Flow Matching via Unbalanced Optimal Transport for Label-free Long-tailed Generation
1341. NeurIPS 2026RGF: Recursive Generative Framework for the Edge-Cut Separable Problems
1342. NeurIPS 2026Robust Flow Matching under Target Corruption and Label Noise
1343. NeurIPS 2026Rotations on Latent Hyperspheres: a Geometry-Aware Guiding Framework for Diffusion Models
1344. NeurIPS 2026Routing as a Singular Reparameterization: A Closed-Form Pushforward Prior and Exact Bayesian Complexity in a Minimal Proxy
1345. NeurIPS 2026Safety Reconstructed: Generative Modeling via Masked Diffusion Builds Strong Safety Guardrails
1346. NeurIPS 2026SAGE: Semantically Disentangled Representation Learning through Latent Geometry Constraint and Large Language Model
1347. NeurIPS 2026Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders
1348. NeurIPS 2026Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
1349. NeurIPS 2026Sample-Efficient Optimization over Generative Priors via Coarse Learnability
1350. NeurIPS 2026SARA: Step-Adaptive Rank Adjustment for Diffusion Inference Acceleration

FAQ

How many Deep learning · generative models and autoencoders papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

4,314 in total: 1,216 in 2023, 768 in 2024, 900 in 2025, 1,430 in 2026.

How many Deep learning · generative models and autoencoders papers are at NeurIPS 2026?

NeurIPS 2026 has 349 Deep learning · generative models and autoencoders papers, versus 240 the year before.

How many Deep learning · generative models and autoencoders papers are at ICLR 2026?

ICLR 2026 has 303 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at ICML 2026?

ICML 2026 has 604 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at CVPR 2026?

CVPR 2026 has 174 Deep learning · generative models and autoencoders papers, versus 110 the year before.

Which conference publishes the most Deep learning · generative models and autoencoders papers?

NeurIPS, with 1,714 papers from 2023 to 2026.