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

900 papers, page 6 of 18

251. ICLR 2025Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
252. ICLR 2025InstantSplamp: Fast and Generalizable Stenography Framework for Generative Gaussian Splatting
253. ICLR 2025Integrative Decoding: Improving Factuality via Implicit Self-consistency
254. ICLR 2025JetFormer: An autoregressive generative model of raw images and text
255. ICLR 2025Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models
256. ICLR 2025LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
257. ICLR 2025LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
258. ICLR 2025Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint
259. ICLR 2025Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
260. ICLR 2025Linear combinations of latents in generative models: subspaces and beyond
261. ICLR 2025Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets
262. ICLR 2025Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding
263. ICLR 2025Lossy Compression with Pretrained Diffusion Models
264. ICLR 2025Making Transformer Decoders Better Differentiable Indexers
265. ICLR 2025Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion
266. ICLR 2025MAP: Multi-Human-Value Alignment Palette
267. ICLR 2025MaRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers
268. ICLR 2025MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
269. ICLR 2025Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling
270. ICLR 2025Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
271. ICLR 2025Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
272. ICLR 2025MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations
273. ICLR 2025Minimal Impact ControlNet: Advancing Multi-ControlNet Integration
274. ICLR 2025MotionDreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer
275. ICLR 2025Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
276. ICLR 2025Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
277. ICLR 2025Multilevel Generative Samplers for Investigating Critical Phenomena
278. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
279. ICLR 2025MuPT: A Generative Symbolic Music Pretrained Transformer
280. ICLR 2025Neural Approximate Mirror Maps for Constrained Diffusion Models
281. ICLR 2025No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models
282. ICLR 2025No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
283. ICLR 2025Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation
284. ICLR 2025Not All Language Model Features Are One-Dimensionally Linear
285. ICLR 2025Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning
286. ICLR 2025NRGBoost: Energy-Based Generative Boosted Trees
287. ICLR 2025O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
288. ICLR 2025On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
289. ICLR 2025On the Feature Learning in Diffusion Models
290. ICLR 2025Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
291. ICLR 2025One Step Diffusion via Shortcut Models
292. ICLR 2025Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
293. ICLR 2025Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
294. ICLR 2025PALMBENCH: A COMPREHENSIVE BENCHMARK OF COMPRESSED LARGE LANGUAGE MODELS ON MOBILE PLATFORMS
295. ICLR 2025ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models
296. ICLR 2025PEARL: Parallel Speculative Decoding with Adaptive Draft Length
297. ICLR 2025PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores
298. ICLR 2025PFGuard: A Generative Framework with Privacy and Fairness Safeguards
299. ICLR 2025PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
300. ICLR 2025Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

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.