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 35 of 87

1701. ICLR 2025Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
1702. ICLR 2025MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations
1703. ICLR 2025Minimal Impact ControlNet: Advancing Multi-ControlNet Integration
1704. ICLR 2025MotionDreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer
1705. ICLR 2025Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
1706. ICLR 2025Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
1707. ICLR 2025Multilevel Generative Samplers for Investigating Critical Phenomena
1708. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
1709. ICLR 2025MuPT: A Generative Symbolic Music Pretrained Transformer
1710. ICLR 2025Neural Approximate Mirror Maps for Constrained Diffusion Models
1711. ICLR 2025No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models
1712. ICLR 2025No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
1713. ICLR 2025Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation
1714. ICLR 2025Not All Language Model Features Are One-Dimensionally Linear
1715. ICLR 2025Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning
1716. ICLR 2025NRGBoost: Energy-Based Generative Boosted Trees
1717. ICLR 2025O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
1718. ICLR 2025On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
1719. ICLR 2025On the Feature Learning in Diffusion Models
1720. ICLR 2025Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
1721. ICLR 2025One Step Diffusion via Shortcut Models
1722. ICLR 2025Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
1723. ICLR 2025Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
1724. ICLR 2025PALMBENCH: A COMPREHENSIVE BENCHMARK OF COMPRESSED LARGE LANGUAGE MODELS ON MOBILE PLATFORMS
1725. ICLR 2025ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models
1726. ICLR 2025PEARL: Parallel Speculative Decoding with Adaptive Draft Length
1727. ICLR 2025PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores
1728. ICLR 2025PFGuard: A Generative Framework with Privacy and Fairness Safeguards
1729. ICLR 2025PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
1730. ICLR 2025Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
1731. ICLR 2025PN-GAIL: Leveraging Non-optimal Information from Imperfect Demonstrations
1732. ICLR 2025PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
1733. ICLR 2025Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
1734. ICLR 2025PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
1735. ICLR 2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models
1736. ICLR 2025Probing the Latent Hierarchical Structure of Data via Diffusion Models
1737. ICLR 2025Progressive Compression with Universally Quantized Diffusion Models
1738. ICLR 2025Proteina: Scaling Flow-based Protein Structure Generative Models
1739. ICLR 2025ProtoSnap: Prototype Alignment For Cuneiform Signs
1740. ICLR 2025QA-Calibration of Language Model Confidence Scores
1741. ICLR 2025Quality Measures for Dynamic Graph Generative Models
1742. ICLR 2025Rapid Selection and Ordering of In-Context Demonstrations via Prompt Embedding Clustering
1743. ICLR 2025RB-Modulation: Training-Free Stylization using Reference-Based Modulation
1744. ICLR 2025Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
1745. ICLR 2025ReGen: Generative Robot Simulation via Inverse Design
1746. ICLR 2025Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds
1747. ICLR 2025RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
1748. ICLR 2025Representative Guidance: Diffusion Model Sampling with Coherence
1749. ICLR 2025Residual Stream Analysis with Multi-Layer SAEs
1750. ICLR 2025Restructuring Vector Quantization with the Rotation Trick

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.