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

768 papers, page 8 of 16

351. ICML 2024Learning Useful Representations of Recurrent Neural Network Weight Matrices
352. ICML 2024Light and Optimal Schrödinger Bridge Matching
353. ICML 2024Listening to the noise: Blind Denoising with Gibbs Diffusion
354. ICML 2024Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
355. ICML 2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
356. ICML 2024Make-A-Shape: a Ten-Million-scale 3D Shape Model
357. ICML 2024Masked Face Recognition with Generative-to-Discriminative Representations
358. ICML 2024Mean-field Chaos Diffusion Models
359. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
360. ICML 2024Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
361. ICML 2024Nearest Neighbour Score Estimators for Diffusion Generative Models
362. ICML 2024Neural Diffusion Models
363. ICML 2024NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
364. ICML 2024ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
365. ICML 2024On Mechanistic Knowledge Localization in Text-to-Image Generative Models
366. ICML 2024On the Role of Edge Dependency in Graph Generative Models
367. ICML 2024OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
368. ICML 2024Partial Optimality in the Linear Ordering Problem
369. ICML 2024Particle Denoising Diffusion Sampler
370. ICML 2024PcLast: Discovering Plannable Continuous Latent States
371. ICML 2024Position: A Safe Harbor for AI Evaluation and Red Teaming
372. ICML 2024Position: Compositional Generative Modeling: A Single Model is Not All You Need
373. ICML 2024Position: Data-driven Discovery with Large Generative Models
374. ICML 2024Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
375. ICML 2024Position: Explain to Question not to Justify
376. ICML 2024Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
377. ICML 2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence
378. ICML 2024Preventing Model Collapse in Gaussian Process Latent Variable Models
379. ICML 2024Pricing with Contextual Elasticity and Heteroscedastic Valuation
380. ICML 2024Projecting Molecules into Synthesizable Chemical Spaces
381. ICML 2024Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
382. ICML 2024Provably Robust DPO: Aligning Language Models with Noisy Feedback
383. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
384. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
385. ICML 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
386. ICML 2024Recovering the Pre-Fine-Tuning Weights of Generative Models
387. ICML 2024Reflected Flow Matching
388. ICML 2024Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
389. ICML 2024Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
390. ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
391. ICML 2024RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
392. ICML 2024Robust and Conjugate Gaussian Process Regression
393. ICML 2024Sample as you Infer: Predictive Coding with Langevin Dynamics
394. ICML 2024Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
395. ICML 2024Self-Correcting Self-Consuming Loops for Generative Model Training
396. ICML 2024Single-Model Attribution of Generative Models Through Final-Layer Inversion
397. ICML 2024Sliced Wasserstein with Random-Path Projecting Directions
398. ICML 2024SMaRt: Improving GANs with Score Matching Regularity
399. ICML 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
400. ICML 2024SqueezeLLM: Dense-and-Sparse Quantization

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.