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 16 of 87

751. ICML 2026Learning Discrete Diffusion on Graphs via Free-Energy Gradient Flows
752. ICML 2026Learning Manifold Data with Flow Matching
753. ICML 2026Learning Permutation Distributions via Reflected Diffusion on Ranks
754. ICML 2026Learning to Remember, Learn, and Forget in Attention-Based Models
755. ICML 2026Learning to Watermark in the Latent Space of Generative Models
756. ICML 2026Learning-to-Optimize via Deep Unfolded Flows
757. ICML 2026LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games
758. ICML 2026Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
759. ICML 2026Let Language Constrain Geometry: Vision–Language Models as Semantic and Spatial Critics for 3D Generation
760. ICML 2026Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
761. ICML 2026LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
762. ICML 2026Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention
763. ICML 2026Likelihood Matching for Diffusion Models
764. ICML 2026Likelihood over Estimation: Robust Quadratic Discriminant Analysis for Heavy-Tailed Distributions with Theory and Evidence
765. ICML 2026LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation
766. ICML 2026LipoPU: Pocket-level Prediction of Lipid-Protein Interactions via Positive-Unlabeled Learning
767. ICML 2026LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
768. ICML 2026Local Mechanisms of Compositional Generalization in Conditional Diffusion
769. ICML 2026Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences
770. ICML 2026Locally Coherent Parallel Decoding in Diffusion Language Models
771. ICML 2026Logical Guidance for the Exact Composition of Diffusion Models
772. ICML 2026LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
773. ICML 2026Lookahead Path Likelihood Optimization for Diffusion LLMs
774. ICML 2026Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
775. ICML 2026Lookahead Unmasking Elicits Reliable Decoding in Diffusion Language Models
776. ICML 2026LUCID: Attention with Preconditioned Representations
777. ICML 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
778. ICML 2026M-Star: Markovian Projection of Star-Shaped Diffusion for Exponential Family Distributions
779. ICML 2026MAD: Manifold Attracted Diffusion
780. ICML 2026Magnitude Distance: A Geometric Measure of Dataset Similarity
781. ICML 2026MAMBO-G: Magnitude-Aware Mitigation for Boosted Guidance
782. ICML 2026Manifold-Aware Perturbations for Constrained Generative Modeling
783. ICML 2026Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
784. ICML 2026MarketSim: Simulating Stock Markets with Large-Scale Generative Agents
785. ICML 2026MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks
786. ICML 2026Maximum-Likelihood Learning of Latent Dynamics Without Reconstruction
787. ICML 2026Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models
788. ICML 2026Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
789. ICML 2026MEDUSA: Motion Elimination in Diffusion Using Spectral Attack
790. ICML 2026MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
791. ICML 2026Meta Flow Maps enable scalable reward alignment
792. ICML 2026Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
793. ICML 2026MetaOthello: A Controlled Study of Multiple World Models in Transformers
794. ICML 2026MidSteer: Optimal Affine Framework for Steering Generative Models
795. ICML 2026Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
796. ICML 2026Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
797. ICML 2026MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
798. ICML 2026MIST: Moment-Aligned Invariant Stability Transform for Robust Flow Matching
799. ICML 2026Mitigating Noise-Induced Layout Priors for Object Counting in Diffusion Models
800. ICML 2026Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization

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.