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

1,430 papers, page 28 of 29

1351. NeurIPS 2026Scattered by Design: Why Per-Output Pruning Resists Structured Compression
1352. NeurIPS 2026Seeing the Unseen: Unified Visible–Invisible Motion for Physically Consistent Video Generation
1353. NeurIPS 2026Selective Safety Steering via Value-Filtered Decoding
1354. NeurIPS 2026Semantic Optimal Transport for Sparse Autoencoder Feature Matching and Circuit Compression
1355. NeurIPS 2026ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens
1356. NeurIPS 2026SOLAR: AI-Powered Speed-of-Light Performance Analysis
1357. NeurIPS 2026SP$^3$: Spherical Priors for Plug-and-Play Restoration
1358. NeurIPS 2026Sparse Biological Features Reveal Early Functional Commitment in Diffusion Protein Language Models
1359. NeurIPS 2026Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
1360. NeurIPS 2026Spectral-Sphere-Constrained Hyper-Connections
1361. NeurIPS 2026Split-and-scale Latent 3D Representations
1362. NeurIPS 2026Stability and Diversity of Networked Self-Consuming Generative Ecosystems
1363. NeurIPS 2026Stable and Granular Policy Optimization for Generative Recommendation
1364. NeurIPS 2026Stable GFlowNets with Probabilistic Guarantees
1365. NeurIPS 2026Stein Transport for Generative Modeling
1366. NeurIPS 2026StiCAS: Compositional Activation Steering via Stiefel Manifold Coordinate Transport
1367. NeurIPS 2026Stochastic Heat Diffusion Models
1368. NeurIPS 2026Stochastic Interpolants via Conditional Dependent Coupling
1369. NeurIPS 2026Structural Rationale Distillation via Reasoning Space Compression
1370. NeurIPS 2026Structured Coupling for Flow Matching
1371. NeurIPS 2026Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
1372. NeurIPS 2026Support Before Frequency in Discrete Diffusion
1373. NeurIPS 2026Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
1374. NeurIPS 2026SVG-3D: Mining Decision Boundaries with Generative Splatting Priors for Zero-Shot 3D Classification
1375. NeurIPS 2026SynMQG: Disentanglement and Mutual-Information Optimization for Synergistic Multi-modal Question Generation
1376. NeurIPS 2026Tabular Foundation Model for Generative Modelling
1377. NeurIPS 2026Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
1378. NeurIPS 2026Taming Generative Co-Folding Prior for Molecular Docking with Diffusion Bridge
1379. NeurIPS 2026Temporal Backtracking Search for Test-time Generative Video Reasoning
1380. NeurIPS 2026Temporal Island Sparse Autoencoders for Interpreting Clinical Time-Series Models
1381. NeurIPS 2026Temporal Pair Consistency for Flow Matching
1382. NeurIPS 2026Tessellations of Semi-Discrete Flow Matching
1383. NeurIPS 2026The $1/\mathcal{W}$ Law: Context Length is the Dominant Energy Lever in LLM Inference Fleets
1384. NeurIPS 2026The balance between feature learning and collapse in generative dynamical systems
1385. NeurIPS 2026The Benefits of Temporal Correlations: SGD Efficiently Learns k-Juntas from Random Walks
1386. NeurIPS 2026The Butterfly Effect in Reasoning: Branch-Structured Distributional Memory for Stochastic Agents
1387. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
1388. NeurIPS 2026The Fault in Our Metrics: Revisiting Generative Model Evaluation with Chamfer Distance
1389. NeurIPS 2026The Rate-Distortion-Polysemanticity Tradeoff in SAEs
1390. NeurIPS 2026The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
1391. NeurIPS 2026Towards Unified Memory Adaptation for LLM Agents: Textual, Latent, and Parametric Pathways
1392. NeurIPS 2026TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
1393. NeurIPS 2026Training-Free Generative Sampling via Moment-Matched Score Smoothing
1394. NeurIPS 2026Transporting Quantiles to Codebook: Scalable Vector Quantization without Codebook Collapse
1395. NeurIPS 2026Tree-Sliced Orlicz Integral Probability Metric
1396. NeurIPS 2026Trie-Aware Transformers for Generative Recommendation
1397. NeurIPS 2026Trivialized Generative Models on Lie Groups
1398. NeurIPS 2026Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
1399. NeurIPS 2026Twisted Schrödinger Bridge Matching
1400. NeurIPS 2026Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent

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.