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 14 of 29

651. ICML 2026FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
652. ICML 2026Fine-Tuning Masked Diffusion for Provable Self-Correction
653. ICML 2026Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation
654. ICML 2026Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
655. ICML 2026Flexibility-Aware Geometric Latent Diffusion for Full-Atom Peptide Design
656. ICML 2026Float8@2bits: Entropy Coding Enables Data-Free Model Compression
657. ICML 2026Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients
658. ICML 2026Flow Sampling : Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
659. ICML 2026FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates
660. ICML 2026FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance For Pruned Large Language Models
661. ICML 2026Focusing: View-Consistent Sparse Voxels for Efficient 3D VAE Training
662. ICML 2026Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
663. ICML 2026Forward-Chaining Temporal Point Process
664. ICML 2026FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
665. ICML 2026FPTQuant: Function-Preserving Transforms for LLM Quantization
666. ICML 2026From Extraction to Deduction: Resolving Functional Misalignment in RAG via a Collaborative Critic-Reasoner Framework
667. ICML 2026From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning
668. ICML 2026From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
669. ICML 2026From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors
670. ICML 2026FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
671. ICML 2026GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
672. ICML 2026GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
673. ICML 2026General and Efficient Steering of Unconditional Diffusion Models
674. ICML 2026Generalization Bounds for Discrete Diffusion: Statistical Advantage of Masking
675. ICML 2026Generalized Discrete Diffusion with Self-Correction
676. ICML 2026Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
677. ICML 2026Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
678. ICML 2026Generative Augmented Inference
679. ICML 2026Generative Inverse Design with Abstention via Diagonal Flow Matching
680. ICML 2026Generative Large Neighborhood Search: Scalable Set Cover Optimization via Discrete Diffusion
681. ICML 2026Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
682. ICML 2026Generative Modeling of Irregular Time Series via SDE-Induced Continuous-Discrete Variational Inference
683. ICML 2026Generative Online Reinforcement Learning
684. ICML 2026Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
685. ICML 2026Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
686. ICML 2026GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
687. ICML 2026Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
688. ICML 2026Geometric Decoupling: Diagnosing the Structural Instability of Latent
689. ICML 2026Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
690. ICML 2026Geometry-Aware Tabular Diffusion
691. ICML 2026Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
692. ICML 2026GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Models
693. ICML 2026GFedCL: Graph-Based Federated Continual Learning with Spatial and Temporal Awareness
694. ICML 2026GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
695. ICML 2026GLAD: Bidirectional Structure-Attribute Alignment via Latent Graph Diffusion Models
696. ICML 2026Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
697. ICML 2026Gradient Preconditioning for Efficient and Reliable Reward-Guided Generation
698. ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
699. ICML 2026GraphP-FL: Personalized Federated Graph Learning via Dynamic Structure Awareness and Fisher Information Elastic Alignment
700. ICML 2026GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning

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.