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 7 of 16

301. NeurIPS 2023Generator Born from Classifier
302. NeurIPS 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
303. NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
304. NeurIPS 2023Geometry-Aware Adaptation for Pretrained Models
305. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
306. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
307. NeurIPS 2023Global Optimality in Bivariate Gradient-based DAG Learning
308. NeurIPS 2023Globally injective and bijective neural operators
309. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
310. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
311. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
312. NeurIPS 2023Grassmann Manifold Flows for Stable Shape Generation
313. NeurIPS 2023Guiding The Last Layer in Federated Learning with Pre-Trained Models
314. NeurIPS 2023H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
315. NeurIPS 2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
316. NeurIPS 2023Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
317. NeurIPS 2023Harnessing Hard Mixed Samples with Decoupled Regularizer
318. NeurIPS 2023HeadSculpt: Crafting 3D Head Avatars with Text
319. NeurIPS 2023Hierarchical clustering with dot products recovers hidden tree structure
320. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
321. NeurIPS 2023High-dimensional Asymptotics of Denoising Autoencoders
322. NeurIPS 2023High-dimensional Contextual Bandit Problem without Sparsity
323. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
324. NeurIPS 2023How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
325. NeurIPS 2023How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
326. NeurIPS 2023How to Turn Your Knowledge Graph Embeddings into Generative Models
327. NeurIPS 2023HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
328. NeurIPS 2023Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
329. NeurIPS 2023HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
330. NeurIPS 2023ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
331. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
332. NeurIPS 2023Im-Promptu: In-Context Composition from Image Prompts
333. NeurIPS 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
334. NeurIPS 2023Imitation Learning from Vague Feedback
335. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
336. NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
337. NeurIPS 2023IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
338. NeurIPS 2023Improved Frequency Estimation Algorithms with and without Predictions
339. NeurIPS 2023Improving Adversarial Transferability via Intermediate-level Perturbation Decay
340. NeurIPS 2023Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
341. NeurIPS 2023Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
342. NeurIPS 2023Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
343. NeurIPS 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
344. NeurIPS 2023In-Context Learning Unlocked for Diffusion Models
345. NeurIPS 2023Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
346. NeurIPS 2023Incentivized Communication for Federated Bandits
347. NeurIPS 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
348. NeurIPS 2023Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
349. NeurIPS 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
350. NeurIPS 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction

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.