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 42 of 87

2051. ICML 2025Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness
2052. ICML 2025Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets
2053. ICML 2025Symmetry-Aware GFlowNets
2054. ICML 2025TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data
2055. ICML 2025TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data
2056. ICML 2025Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
2057. ICML 2025Teaching Language Models to Critique via Reinforcement Learning
2058. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
2059. ICML 2025Testing Conditional Mean Independence Using Generative Neural Networks
2060. ICML 2025The Diffusion Duality
2061. ICML 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
2062. ICML 2025ToMA: Token Merge with Attention for Diffusion Models
2063. ICML 2025Towards a Mechanistic Explanation of Diffusion Model Generalization
2064. ICML 2025Towards a Unified Framework of Clustering-based Anomaly Detection
2065. ICML 2025Tractable Transformers for Flexible Conditional Generation
2066. ICML 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
2067. ICML 2025Training Diffusion-based Generative Models with Limited Data
2068. ICML 2025Tree-Sliced Wasserstein Distance with Nonlinear Projection
2069. ICML 2025Tree-Sliced Wasserstein Distance: A Geometric Perspective
2070. ICML 2025Ultra-Resolution Adaptation with Ease
2071. ICML 2025Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
2072. ICML 2025Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
2073. ICML 2025UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
2074. ICML 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
2075. ICML 2025Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
2076. ICML 2025Variational Control for Guidance in Diffusion Models
2077. ICML 2025Variational Learning of Fractional Posteriors
2078. ICML 2025Variational Rectified Flow Matching
2079. ICML 2025VCT: Training Consistency Models with Variational Noise Coupling
2080. ICML 2025Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
2081. ICML 2025VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
2082. ICML 2025VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
2083. ICML 2025Visual Generation Without Guidance
2084. ICML 2025Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
2085. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
2086. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
2087. ICML 2025WMAdapter: Adding WaterMark Control to Latent Diffusion Models
2088. ICML 2025Wyckoff Transformer: Generation of Symmetric Crystals
2089. ICML 2025Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
2090. ICML 2025ZipAR: Parallel Autoregressive Image Generation through Spatial Locality
2091. NeurIPS 20254D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time
2092. NeurIPS 2025A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
2093. NeurIPS 2025A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models
2094. NeurIPS 2025A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
2095. NeurIPS 2025A solvable model of learning generative diffusion: theory and insights
2096. NeurIPS 2025Accelerating 3D Molecule Generative Models with Trajectory Diagnosis
2097. NeurIPS 2025Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction
2098. NeurIPS 2025Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
2099. NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
2100. NeurIPS 2025Alchemist: Turning Public Text-to-Image Data into Generative Gold

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.