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

1,430 papers, page 15 of 29

701. ICML 2026GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance
702. ICML 2026Hard-Constrained Graph Generation with Discrete-Projection Diffusion
703. ICML 2026HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
704. ICML 2026Heterogeneous Customizable Personalized Federated Fine-Tuning Approach for Large Language Models
705. ICML 2026High-accuracy and dimension-free sampling with diffusions
706. ICML 2026High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders
707. ICML 2026HilbertA: Hilbert-Curve–Aligned Sparse Attention for 2D Structured Data
708. ICML 2026HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation
709. ICML 2026Hölder++: Improving Quality-Coherence Trade-off in Multimodal VAEs
710. ICML 2026How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
711. ICML 2026Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
712. ICML 2026Identifiable Equivariant Networks are Layerwise Equivariant
713. ICML 2026IDLM: Inverse-distilled Diffusion Language Models
714. ICML 2026Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness
715. ICML 2026Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
716. ICML 2026Improving LLM-Based Recommenders with Conservative Generative Flow Networks
717. ICML 2026Improving Sampling for Masked Diffusion Models via Information Gain
718. ICML 2026In-Context Generation with Regional Constraints for Instructional Video Editing
719. ICML 2026In-Context Universal Approximation, Compositional Generalization, and Algorithm Emulation
720. ICML 2026Independent Component Discovery in Temporal Count Data
721. ICML 2026InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
722. ICML 2026Inference-time optimization for experiment-grounded protein ensemble generation
723. ICML 2026Infinite Mask Diffusion for Few-Step Distillation
724. ICML 2026Infinite-Dimensional Generative Diffusions via Doob’s h-Transform
725. ICML 2026Infinite-Precision Autoregressive Modeling for Vector Graphics and Layouts
726. ICML 2026Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
727. ICML 2026Information-Geometric Adaptive Sampling for Graph Diffusion
728. ICML 2026Information-Theoretic Generalization Bounds for VAEs: A Role of Encoder and Latent Variable
729. ICML 2026InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
730. ICML 2026Insertion Based Sequence Generation with Learnable Order Dynamics
731. ICML 2026INT vs. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
732. ICML 2026Integrated Episodic and Semantic Memory via Modulating Transformer FeedForward Layers
733. ICML 2026Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
734. ICML 2026Interpreting Genomic Language Models using Sparse Autoencoders
735. ICML 2026Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
736. ICML 2026Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
737. ICML 2026Is Generation Required for Data-Efficient Perception?
738. ICML 2026Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models
739. ICML 2026Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
740. ICML 2026Just Y-Prediction: Enabling Historical Cumulative Inconsistency in Label Diffusion for Learning with Noisy Label
741. ICML 2026KIStego: Key-Independent Secure Image Distribution via Bipartite Structural Invariants
742. ICML 2026Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
743. ICML 2026Language Generation in the Limit: Complexity Barriers and Implications for Learning
744. ICML 2026Large-capacity and Receiver Authenticable Generative Image Steganography
745. ICML 2026Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
746. ICML 2026Latent-Guided Cooperative Energy-Based Models
747. ICML 2026LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
748. ICML 2026LayerT2V: A Unified Multi-Layer Video Generation Framework
749. ICML 2026Learning a Generative Meta-Model of LLM Activations
750. ICML 2026Learning Coherent Representations: A Topological Approach to Interpretability

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.