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

830 papers, page 6 of 17

251. ICLR 2026Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
252. ICLR 2026SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
253. ICLR 2026SpikeGen: Decoupled “Rods and Cones” Visual Representation Processing with Latent Generative Framework
254. ICLR 2026Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models
255. ICLR 2026STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
256. ICLR 2026STORK: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence
257. ICLR 2026Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
258. ICLR 2026Subquadratic Algorithms and Hardness for Attention with Any Temperature
259. ICLR 2026Symmetric Space Learning for Combinatorial Generalization
260. ICLR 2026Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
261. ICLR 2026Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models
262. ICLR 2026Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
263. ICLR 2026Taming Polysemanticity in LLMs: Theory-Grounded Feature Recovery via Sparse Autoencoders
264. ICLR 2026Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
265. ICLR 2026TAVAE: A VAE with Adaptable Priors Explains Contextual Modulation in the Visual Cortex
266. ICLR 2026Teaching Metric Distance to Discrete Autoregressive Language Models
267. ICLR 2026Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
268. ICLR 2026Terminal Velocity Matching
269. ICLR 2026Test-Time Scaling with Reflective Generative Model
270. ICLR 2026Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
271. ICLR 2026The Diffusion Duality, Chapter II: $\Psi$-Samplers and Efficient Curriculum
272. ICLR 2026The Intricate Dance of Prompt Complexity, Quality, Diversity and Consistency in T2I Models
273. ICLR 2026The Lattice Geometry of Neural Network Quantization: A Short Equivalence Proof of GPTQ and Babai's Algorithm
274. ICLR 2026The Price of Amortized inference in Sparse Autoencoders
275. ICLR 2026The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology
276. ICLR 2026The Spacetime of Diffusion Models: An Information Geometry Perspective
277. ICLR 2026There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models
278. ICLR 2026Think Then Embed: Generative Context Improves Multimodal Embedding
279. ICLR 2026Time-Gated Multi-Scale Flow Matching for Time-Series Imputation
280. ICLR 2026ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing
281. ICLR 2026Topological Flow Matching
282. ICLR 2026Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders
283. ICLR 2026Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
284. ICLR 2026TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models
285. ICLR 2026Tree-sliced Sobolev IPM
286. ICLR 2026TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models
287. ICLR 2026Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
288. ICLR 2026Two-Layer Convolutional Autoencoders Trained on Normal Data Provably Detect Unseen Anomalies
289. ICLR 2026Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
290. ICLR 2026Unsupervised Representation Learning for 3D Mesh Parameterization with Semantic and Visibility Objectives
291. ICLR 2026Untraceable DeepFakes via Traceable Fingerprint Elimination
292. ICLR 2026Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling
293. ICLR 2026Verification of the Implicit World Model in a Generative Model via Adversarial Sequences
294. ICLR 2026VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
295. ICLR 2026What Exactly Does Guidance Do in Masked Discrete Diffusion Models
296. ICLR 2026What Generative Search Engines Like and How to Optimize Web Content Cooperatively
297. ICLR 2026What happens when generative AI models train recursively on each others' outputs?
298. ICLR 2026When LLMs get significantly worse: A statistical approach to detect model degradations
299. ICLR 2026When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
300. ICLR 2026WithAnyone: Toward Controllable and ID Consistent Image Generation

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.