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 10 of 16

451. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
452. NeurIPS 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
453. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
454. NeurIPS 2023NAP: Neural 3D Articulated Object Prior
455. NeurIPS 2023NAS-X: Neural Adaptive Smoothing via Twisting
456. NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
457. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
458. NeurIPS 2023Near-optimal learning with average Hölder smoothness
459. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
460. NeurIPS 2023Neural Image Compression: Generalization, Robustness, and Spectral Biases
461. NeurIPS 2023Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
462. NeurIPS 2023Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
463. NeurIPS 2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
464. NeurIPS 2023Neural Sampling in Hierarchical Exponential-family Energy-based Models
465. NeurIPS 2023NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
466. NeurIPS 2023Neuro-symbolic Learning Yielding Logical Constraints
467. NeurIPS 2023NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
468. NeurIPS 2023No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
469. NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
470. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
471. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
472. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
473. NeurIPS 2023Nonparametric Boundary Geometry in Physics Informed Deep Learning
474. NeurIPS 2023Normalization-Equivariant Neural Networks with Application to Image Denoising
475. NeurIPS 2023NPCL: Neural Processes for Uncertainty-Aware Continual Learning
476. NeurIPS 2023Object-centric Learning with Cyclic Walks between Parts and Whole
477. NeurIPS 2023Object-Centric Slot Diffusion
478. NeurIPS 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
479. NeurIPS 2023On Calibrating Diffusion Probabilistic Models
480. NeurIPS 2023On Learning Latent Models with Multi-Instance Weak Supervision
481. NeurIPS 2023On Measuring Fairness in Generative Models
482. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
483. NeurIPS 2023On quantum backpropagation, information reuse, and cheating measurement collapse
484. NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
485. NeurIPS 2023On Single-Index Models beyond Gaussian Data
486. NeurIPS 2023On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
487. NeurIPS 2023On the Constrained Time-Series Generation Problem
488. NeurIPS 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
489. NeurIPS 2023On the impact of activation and normalization in obtaining isometric embeddings at initialization
490. NeurIPS 2023On the Role of Entanglement and Statistics in Learning
491. NeurIPS 2023On the Role of Randomization in Adversarially Robust Classification
492. NeurIPS 2023On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
493. NeurIPS 2023On the Variance, Admissibility, and Stability of Empirical Risk Minimization
494. NeurIPS 2023On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models
495. NeurIPS 2023One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
496. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
497. NeurIPS 2023One-Step Diffusion Distillation via Deep Equilibrium Models
498. NeurIPS 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
499. NeurIPS 2023Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
500. NeurIPS 2023Online Clustering of Bandits with Misspecified User Models

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.