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

1,714 papers, page 10 of 35

451. NeurIPS 2025How many measurements are enough? Bayesian recovery in inverse problems with general distributions
452. NeurIPS 2025Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
453. NeurIPS 2025Image Super-Resolution with Guarantees via Conformalized Generative Models
454. NeurIPS 2025Implicit Generative Property Enhancer
455. NeurIPS 2025Improved Training Technique for Shortcut Models
456. NeurIPS 2025Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
457. NeurIPS 2025Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads
458. NeurIPS 2025In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain
459. NeurIPS 2025Inference with correlated priors using sisters cells
460. NeurIPS 2025Information Theoretic Learning for Diffusion Models with Warm Start
461. NeurIPS 2025Informed Correctors for Discrete Diffusion Models
462. NeurIPS 2025Is Your Diffusion Model Actually Denoising?
463. NeurIPS 2025JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemble Generation
464. NeurIPS 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation
465. NeurIPS 2025KLASS: KL-Guided Fast Inference in Masked Diffusion Models
466. NeurIPS 2025Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning
467. NeurIPS 2025Knowledge Starts with Practice: Knowledge-Aware Exercise Generative Recommendation with Adaptive Multi-Agent Cooperation
468. NeurIPS 2025Large Language Diffusion Models
469. NeurIPS 2025Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
470. NeurIPS 2025Learning conformational ensembles of proteins based on backbone geometry
471. NeurIPS 2025Learning Diffusion Models with Flexible Representation Guidance
472. NeurIPS 2025Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
473. NeurIPS 2025Learning to Add, Multiply, and Execute Algorithmic Instructions Exactly with Neural Networks
474. NeurIPS 2025Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
475. NeurIPS 2025Learning to Integrate Diffusion ODEs by Averaging the Derivatives
476. NeurIPS 2025Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions
477. NeurIPS 2025LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss
478. NeurIPS 2025Locality in Image Diffusion Models Emerges from Data Statistics
479. NeurIPS 2025Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
480. NeurIPS 2025Low Rank Gradients and Where to Find Them
481. NeurIPS 2025Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
482. NeurIPS 2025Mean Flows for One-step Generative Modeling
483. NeurIPS 2025Measuring and Guiding Monosemanticity
484. NeurIPS 2025Missing Data Imputation by Reducing Mutual Information with Rectified Flows
485. NeurIPS 2025MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
486. NeurIPS 2025Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data
487. NeurIPS 2025Multivariate Time Series Anomaly Detection with Idempotent Reconstruction
488. NeurIPS 2025Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
489. NeurIPS 2025Neural Correlates of Serial Dependence: Synaptic Short-term Plasticity Orchestrates Repulsion and Attraction
490. NeurIPS 2025Non-Markovian Discrete Diffusion with Causal Language Models
491. NeurIPS 2025On scalable and efficient training of diffusion samplers
492. NeurIPS 2025On the Relation between Rectified Flows and Optimal Transport
493. NeurIPS 2025One-Step Diffusion-Based Image Compression with Semantic Distillation
494. NeurIPS 2025Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization
495. NeurIPS 2025Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation
496. NeurIPS 2025Orientation Matters: Making 3D Generative Models Orientation-Aligned
497. NeurIPS 2025PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
498. NeurIPS 2025PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
499. NeurIPS 2025PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
500. NeurIPS 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation

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.