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

349 papers, page 7 of 7

301. NeurIPS 2026Tessellations of Semi-Discrete Flow Matching
302. NeurIPS 2026The $1/\mathcal{W}$ Law: Context Length is the Dominant Energy Lever in LLM Inference Fleets
303. NeurIPS 2026The balance between feature learning and collapse in generative dynamical systems
304. NeurIPS 2026The Benefits of Temporal Correlations: SGD Efficiently Learns k-Juntas from Random Walks
305. NeurIPS 2026The Butterfly Effect in Reasoning: Branch-Structured Distributional Memory for Stochastic Agents
306. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
307. NeurIPS 2026The Fault in Our Metrics: Revisiting Generative Model Evaluation with Chamfer Distance
308. NeurIPS 2026The Rate-Distortion-Polysemanticity Tradeoff in SAEs
309. NeurIPS 2026The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
310. NeurIPS 2026Towards Unified Memory Adaptation for LLM Agents: Textual, Latent, and Parametric Pathways
311. NeurIPS 2026TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
312. NeurIPS 2026Training-Free Generative Sampling via Moment-Matched Score Smoothing
313. NeurIPS 2026Transporting Quantiles to Codebook: Scalable Vector Quantization without Codebook Collapse
314. NeurIPS 2026Tree-Sliced Orlicz Integral Probability Metric
315. NeurIPS 2026Trie-Aware Transformers for Generative Recommendation
316. NeurIPS 2026Trivialized Generative Models on Lie Groups
317. NeurIPS 2026Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
318. NeurIPS 2026Twisted Schrödinger Bridge Matching
319. NeurIPS 2026Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent
320. NeurIPS 2026UGGRH: Unsupervised Generative Completion and Graph-attention Refinement for Incomplete Cross-modal Hashing
321. NeurIPS 2026Understanding diffusion models requires rethinking (again) generalization
322. NeurIPS 2026Understanding the Challenges in Iterative Generative Optimization with LLMs
323. NeurIPS 2026Understanding the Interplay between Memorization and Learning in Large Language Models
324. NeurIPS 2026Unified Generative-Predictive Modeling for 4D Scene Understanding
325. NeurIPS 2026Uniform Diffusion Models revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
326. NeurIPS 2026Uniform Stability and Generalization Error of GD and SGD on Fixed-Point Parameters
327. NeurIPS 2026UniVer: A Unified Perspective for Multi-step and Multi-draft Speculative Decoding
328. NeurIPS 2026Unleashing the Power of Intrinsic-Entropy-Driven Exploration for Off-Policy Generative RL
329. NeurIPS 2026Unlocking the Duality between Flow and Field Matching
330. NeurIPS 2026Variable-Length Generative Protein Design via Generalized Poisson Flow
331. NeurIPS 2026Vermeer: Autoregressive generative modeling of microscopy predicts protein localization
332. NeurIPS 2026VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
333. NeurIPS 2026Wavelet Flow Matching for Multi-Scale Physics Emulation
334. NeurIPS 2026What Can Labels Alone Audit and Edit in Frozen Representations?
335. NeurIPS 2026What Cohort INRs Encode, and Where to Freeze Them
336. NeurIPS 2026What Does a Sparse Autoencoder Feature Do? A Weight-Based Account
337. NeurIPS 2026What Drives Compositional Generalization? The Importance of Continuous Training Objectives in Visual Generative Models
338. NeurIPS 2026What Makes a Good Path? Factoring Manifold Support and Path Geometry
339. NeurIPS 2026What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
340. NeurIPS 2026When and How to Canonize: a Generalization Perspective
341. NeurIPS 2026When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
342. NeurIPS 2026When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds
343. NeurIPS 2026When Are Compositional Problems Learnable from Verifiable Rewards?
344. NeurIPS 2026When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors
345. NeurIPS 2026When Less is More: The LLM Scaling Paradox in Context Compression
346. NeurIPS 2026When Metropolis and Hastings Meet Bradley and Terry: Exact MCMC From Preference Voting
347. NeurIPS 2026Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models
348. NeurIPS 2026Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-Training
349. NeurIPS 2026WorldComposer: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation

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.