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 9 of 87

401. ICLR 2026Scaling Speech Tokenizers with Diffusion Autoencoders
402. ICLR 2026ScalingCache: Extreme Acceleration of DiTs through Difference Scaling and Dynamic Interval Caching
403. ICLR 2026SCOPED: Score–Curvature Out-of-distribution Proximity Evaluator for Diffusion
404. ICLR 2026Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
405. ICLR 2026Self-Speculative Decoding Accelerates Lossless Inference in Any-Order and Any-Subset Autoregressive Models
406. ICLR 2026Self-Speculative Masked Diffusions
407. ICLR 2026SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation
408. ICLR 2026SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
409. ICLR 2026SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
410. ICLR 2026Shift-and-Sum Quantization for Visual Autoregressive Models
411. ICLR 2026Shrinking Proteins with Diffusion
412. ICLR 2026SIGMA-GEN: STRUCTURE AND IDENTITY GUIDED MULTI-SUBJECT ASSEMBLY FOR IMAGE GENERATION
413. ICLR 2026Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
414. ICLR 2026SimpleFold: Folding Proteins is Simpler than You Think
415. ICLR 2026SimpleGVR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution
416. ICLR 2026SoFlow: Solution Flow Models for One-Step Generative Modeling
417. ICLR 2026SONA: Learning Conditional, Unconditional, and Matching-Aware Discriminator
418. ICLR 2026SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
419. ICLR 2026Sparse Autoencoders Trained on the Same Data Learn Different Features
420. ICLR 2026Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
421. ICLR 2026SparseD: Sparse Attention for Diffusion Language Models
422. ICLR 2026SpatialHand: Generative Object Manipulation from 3D Prespective
423. ICLR 2026Spatially Informed Autoencoders for Interpretable Visual Representation Learning
424. ICLR 2026SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism
425. ICLR 2026Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
426. ICLR 2026SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
427. ICLR 2026SpikeGen: Decoupled “Rods and Cones” Visual Representation Processing with Latent Generative Framework
428. ICLR 2026Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models
429. ICLR 2026STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
430. ICLR 2026STORK: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence
431. ICLR 2026Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
432. ICLR 2026Subquadratic Algorithms and Hardness for Attention with Any Temperature
433. ICLR 2026Symmetric Space Learning for Combinatorial Generalization
434. ICLR 2026Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
435. ICLR 2026Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models
436. ICLR 2026Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
437. ICLR 2026Taming Polysemanticity in LLMs: Theory-Grounded Feature Recovery via Sparse Autoencoders
438. ICLR 2026Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
439. ICLR 2026TAVAE: A VAE with Adaptable Priors Explains Contextual Modulation in the Visual Cortex
440. ICLR 2026Teaching Metric Distance to Discrete Autoregressive Language Models
441. ICLR 2026Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
442. ICLR 2026Terminal Velocity Matching
443. ICLR 2026Test-Time Scaling with Reflective Generative Model
444. ICLR 2026Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
445. ICLR 2026The Diffusion Duality, Chapter II: $\Psi$-Samplers and Efficient Curriculum
446. ICLR 2026The Intricate Dance of Prompt Complexity, Quality, Diversity and Consistency in T2I Models
447. ICLR 2026The Lattice Geometry of Neural Network Quantization: A Short Equivalence Proof of GPTQ and Babai's Algorithm
448. ICLR 2026The Price of Amortized inference in Sparse Autoencoders
449. ICLR 2026The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology
450. ICLR 2026The Spacetime of Diffusion Models: An Information Geometry Perspective

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.