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 68 of 87

3351. ICML 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
3352. ICML 2023FARE: Provably Fair Representation Learning with Practical Certificates
3353. ICML 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
3354. ICML 2023FeDXL: Provable Federated Learning for Deep X-Risk Optimization
3355. ICML 2023Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
3356. ICML 2023Few-Sample Feature Selection via Feature Manifold Learning
3357. ICML 2023Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
3358. ICML 2023FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
3359. ICML 2023Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
3360. ICML 2023FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
3361. ICML 2023FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
3362. ICML 2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
3363. ICML 2023From Perception to Programs: Regularize, Overparameterize, and Amortize
3364. ICML 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
3365. ICML 2023Fully Dynamic Submodular Maximization over Matroids
3366. ICML 2023Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
3367. ICML 2023Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
3368. ICML 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
3369. ICML 2023Generative Adversarial Symmetry Discovery
3370. ICML 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
3371. ICML 2023Generative Decoding of Visual Stimuli
3372. ICML 2023Generative Graph Dictionary Learning
3373. ICML 2023Generative Pretraining for Black-Box Optimization
3374. ICML 2023Geometric Autoencoders - What You See is What You Decode
3375. ICML 2023GFlowOut: Dropout with Generative Flow Networks
3376. ICML 2023Go Beyond Imagination: Maximizing Episodic Reachability with World Models
3377. ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
3378. ICML 2023Graphically Structured Diffusion Models
3379. ICML 2023Grounding Language Models to Images for Multimodal Inputs and Outputs
3380. ICML 2023Guiding Pretraining in Reinforcement Learning with Large Language Models
3381. ICML 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
3382. ICML 2023Hierarchical Diffusion for Offline Decision Making
3383. ICML 2023Hierarchical Imitation Learning with Vector Quantized Models
3384. ICML 2023High Fidelity Image Counterfactuals with Probabilistic Causal Models
3385. ICML 2023Hindsight Learning for MDPs with Exogenous Inputs
3386. ICML 2023How much does Initialization Affect Generalization?
3387. ICML 2023Hyperbolic Representation Learning: Revisiting and Advancing
3388. ICML 2023Hyperparameters in Reinforcement Learning and How To Tune Them
3389. ICML 2023Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
3390. ICML 2023Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
3391. ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
3392. ICML 2023Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
3393. ICML 2023Individually Fair Learning with One-Sided Feedback
3394. ICML 2023Inflow, Outflow, and Reciprocity in Machine Learning
3395. ICML 2023Input Perturbation Reduces Exposure Bias in Diffusion Models
3396. ICML 2023Jump-Start Reinforcement Learning
3397. ICML 2023KDEformer: Accelerating Transformers via Kernel Density Estimation
3398. ICML 2023Latent Traversals in Generative Models as Potential Flows
3399. ICML 2023LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
3400. ICML 2023Learn to Accumulate Evidence from All Training Samples: Theory and Practice

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.