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 26 of 87

1251. NeurIPS 2026LLM-Auction: Generative Auction towards LLM-Native Advertising
1252. NeurIPS 2026Localizing and Repairing Sparse-Prompt Failure in SAM Decoders via Box-to-Point Counterfactual
1253. NeurIPS 2026Looking Through the Mirror: Minimax-Optimal Regularized Regrets in Online Learning and Bandits
1254. NeurIPS 2026Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
1255. NeurIPS 2026LoRAtorio: An intrinsic approach to LoRA Skill Composition
1256. NeurIPS 2026Magnetic Resonance Unpaired Image Translation with Pseudometric Schrödinger Bridges
1257. NeurIPS 2026Masked Generative Pretraining Improves Cross-Dataset Transfer in Pixel-Space Diffusion
1258. NeurIPS 2026Matryoshka Transcoders and Hierarchy Misalignment: When SAE Absorption Protection Does Not Transfer
1259. NeurIPS 2026Meta Inverse Prompting for Video Generative Models
1260. NeurIPS 2026MIND: Monge Inception Distance for Generative Models Evaluation
1261. NeurIPS 2026MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
1262. NeurIPS 2026MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series
1263. NeurIPS 2026Mixed-Curvature Geometric Latent Diffusion Model for Graph Generation
1264. NeurIPS 2026Model Collapse is a Singular Complexity Trajectory
1265. NeurIPS 2026Model-Based Online Decision Making via Generative Trajectory Planning
1266. NeurIPS 2026Module Specialization in Transformer Factual Recall under Correlated Fact Distributions
1267. NeurIPS 2026MOSAIC: Scaling Long-Horizon Language Agents via Multi-Scale Adaptive Inference Control
1268. NeurIPS 2026Multi-step Consistency Models: A Complete Error Theory and Optimal Step Selection
1269. NeurIPS 2026Multilevel and Sequential Monte Carlo for Training-Free Diffusion Guidance
1270. NeurIPS 2026NAGO: Noise-Aware Generative Operator via Flow Matching
1271. NeurIPS 2026Nahual: A Sequence Model for Language and Atoms
1272. NeurIPS 2026Network Learning with Semi-relaxed Gromov-Wasserstein
1273. NeurIPS 2026Neural Backward Filtering Forward Guiding
1274. NeurIPS 2026Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction
1275. NeurIPS 2026Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries
1276. NeurIPS 2026Neuromodulated Constrained Autoencoders for Context-Dependent Manifold Learning
1277. NeurIPS 2026NIV: Neural Axis Variations for Variable Font Generation
1278. NeurIPS 2026No Model Required: Text Entropy Rate Filtering Prevents Iterative Fine-Tuning Collapse
1279. NeurIPS 2026Noise-Started One-Step Image Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
1280. NeurIPS 2026Noisy2Latent: Nonparametric Deconvolution and Denoising via Maximum Mean Discrepancy
1281. NeurIPS 2026Non-Expanding Gated Continued Fraction Architecture for Feedforward Layers in Language Models
1282. NeurIPS 2026Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers
1283. NeurIPS 2026Normalizing Trajectory Models
1284. NeurIPS 2026Not Suppressing or Purifying: Backdoor Containment via Expert Quarantine and Shutdown in LLMs
1285. NeurIPS 2026Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot
1286. NeurIPS 2026On approximation and estimation of Schrödinger potentials without the curse of dimensionality
1287. NeurIPS 2026On the Depth of Monotone ReLU Neural Networks and ICNNs
1288. NeurIPS 2026On the Selectivity of Generative Models in Structure-Based Drug Design
1289. NeurIPS 2026On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
1290. NeurIPS 2026On the Tightness and Computational Tractability of Higher-Dimensional Confidence Sequences
1291. NeurIPS 2026One-Shot Generative Flows: Existence and Obstructions
1292. NeurIPS 2026OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework
1293. NeurIPS 2026Optimistic Dual Averaging Unifies Modern Optimizers
1294. NeurIPS 2026Order-Optimal Sample Complexity for Distribution Learning via Flow Matching
1295. NeurIPS 2026Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering
1296. NeurIPS 2026PatchBench: Measuring Collateral Damage in Activation Patching
1297. NeurIPS 2026Patching Up Circuit Learning on Images
1298. NeurIPS 2026Path-independent Flow Matching for Multi-parameter Generative Dynamics
1299. NeurIPS 2026PEEK: One-Step Look-Ahead Exposure Bias Correction for Diffusion Sampling
1300. NeurIPS 2026Permutation-Invariant Spectral Learning via Dyson Diffusion

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.