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

776 papers, page 8 of 16

351. NeurIPS 2023Invariant Learning via Probability of Sufficient and Necessary Causes
352. NeurIPS 2023Inverse Preference Learning: Preference-based RL without a Reward Function
353. NeurIPS 2023Is Distance Matrix Enough for Geometric Deep Learning?
354. NeurIPS 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
355. NeurIPS 2023Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning
356. NeurIPS 2023Iterative Reachability Estimation for Safe Reinforcement Learning
357. NeurIPS 2023Iteratively Learn Diverse Strategies with State Distance Information
358. NeurIPS 2023Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
359. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
360. NeurIPS 2023Joint Attribute and Model Generalization Learning for Privacy-Preserving Action Recognition
361. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
362. NeurIPS 2023Joint Training of Deep Ensembles Fails Due to Learner Collusion
363. NeurIPS 2023JourneyDB: A Benchmark for Generative Image Understanding
364. NeurIPS 2023KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
365. NeurIPS 2023Katakomba: Tools and Benchmarks for Data-Driven NetHack
366. NeurIPS 2023Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
367. NeurIPS 2023Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
368. NeurIPS 2023Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
369. NeurIPS 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
370. NeurIPS 2023Label-Only Model Inversion Attacks via Knowledge Transfer
371. NeurIPS 2023Labeling Neural Representations with Inverse Recognition
372. NeurIPS 2023Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
373. NeurIPS 2023Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
374. NeurIPS 2023LayoutPrompter: Awaken the Design Ability of Large Language Models
375. NeurIPS 2023Learning a 1-layer conditional generative model in total variation
376. NeurIPS 2023Learning and Collusion in Multi-unit Auctions
377. NeurIPS 2023Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
378. NeurIPS 2023Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
379. NeurIPS 2023Learning Dictionary for Visual Attention
380. NeurIPS 2023Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
381. NeurIPS 2023Learning Energy-based Model via Dual-MCMC Teaching
382. NeurIPS 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
383. NeurIPS 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
384. NeurIPS 2023Learning Exponential Families from Truncated Samples
385. NeurIPS 2023Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
386. NeurIPS 2023Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
387. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
388. NeurIPS 2023Learning Mixtures of Gaussians Using the DDPM Objective
389. NeurIPS 2023Learning Modulated Transformation in GANs
390. NeurIPS 2023Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations
391. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
392. NeurIPS 2023Learning Nonparametric Latent Causal Graphs with Unknown Interventions
393. NeurIPS 2023Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
394. NeurIPS 2023Learning threshold neurons via edge of stability
395. NeurIPS 2023Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
396. NeurIPS 2023Learning to Compress Prompts with Gist Tokens
397. NeurIPS 2023Learning to Configure Separators in Branch-and-Cut
398. NeurIPS 2023Learning To Dive In Branch And Bound
399. NeurIPS 2023Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
400. NeurIPS 2023Learning to Reason and Memorize with Self-Notes

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.