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

768 papers, page 9 of 16

401. ICML 2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
402. ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
403. ICML 2024Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
404. ICML 2024Stochastic Interpolants with Data-Dependent Couplings
405. ICML 2024StrWAEs to Invariant Representations
406. ICML 2024Switched Flow Matching: Eliminating Singularities via Switching ODEs
407. ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
408. ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
409. ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
410. ICML 2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline
411. ICML 2024Timer: Generative Pre-trained Transformers Are Large Time Series Models
412. ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
413. ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
414. ICML 2024Towards Neural Architecture Search through Hierarchical Generative Modeling
415. ICML 2024Towards Scalable and Versatile Weight Space Learning
416. ICML 2024Towards Theoretical Understandings of Self-Consuming Generative Models
417. ICML 2024Transformers, parallel computation, and logarithmic depth
418. ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
419. ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?
420. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
421. NeurIPS 20244Diffusion: Multi-view Video Diffusion Model for 4D Generation
422. NeurIPS 20244M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
423. NeurIPS 20244Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models
424. NeurIPS 2024A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization
425. NeurIPS 2024A Closer Look at AUROC and AUPRC under Class Imbalance
426. NeurIPS 2024A Functional Extension of Semi-Structured Networks
427. NeurIPS 2024A Generative Model of Symmetry Transformations
428. NeurIPS 2024A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
429. NeurIPS 2024A scalable generative model for dynamical system reconstruction from neuroimaging data
430. NeurIPS 2024A Simple and Optimal Approach for Universal Online Learning with Gradient Variations
431. NeurIPS 2024A Theoretical Understanding of Self-Correction through In-context Alignment
432. NeurIPS 2024A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis
433. NeurIPS 2024Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences
434. NeurIPS 2024ACES: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models
435. NeurIPS 2024Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
436. NeurIPS 2024Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models
437. NeurIPS 2024Adversarial Schrödinger Bridge Matching
438. NeurIPS 2024AFBench: A Large-scale Benchmark for Airfoil Design
439. NeurIPS 2024AirSketch: Generative Motion to Sketch
440. NeurIPS 2024Aligning Diffusion Models by Optimizing Human Utility
441. NeurIPS 2024An Equivalence Between Static and Dynamic Regret Minimization
442. NeurIPS 2024An Image is Worth 32 Tokens for Reconstruction and Generation
443. NeurIPS 2024Animate3D: Animating Any 3D Model with Multi-view Video Diffusion
444. NeurIPS 2024Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
445. NeurIPS 2024Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting
446. NeurIPS 2024Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
447. NeurIPS 2024ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction
448. NeurIPS 2024Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication
449. NeurIPS 2024Autoregressive Image Generation without Vector Quantization
450. NeurIPS 2024Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs

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.