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

900 papers, page 12 of 18

551. ICML 2025MODA: MOdular Duplex Attention for Multimodal Perception, Cognition, and Emotion Understanding
552. ICML 2025Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
553. ICML 2025Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models
554. ICML 2025Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion
555. ICML 2025Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference
556. ICML 2025MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
557. ICML 2025Near-Optimal Sample Complexity for MDPs via Anchoring
558. ICML 2025Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
559. ICML 2025NETS: A Non-equilibrium Transport Sampler
560. ICML 2025NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones
561. ICML 2025Noise Conditional Variational Score Distillation
562. ICML 2025Normalizing Flows are Capable Generative Models
563. ICML 2025NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
564. ICML 2025OmiAD: One-Step Adaptive Masked Diffusion Model for Multi-class Anomaly Detection via Adversarial Distillation
565. ICML 2025On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
566. ICML 2025On the Guidance of Flow Matching
567. ICML 2025Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models
568. ICML 2025Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
569. ICML 2025Perceptually Constrained Precipitation Nowcasting Model
570. ICML 2025Physics-Informed Generative Modeling of Wireless Channels
571. ICML 2025PiD: Generalized AI-Generated Images Detection with Pixelwise Decomposition Residuals
572. ICML 2025PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
573. ICML 2025Position: All Current Generative Fidelity and Diversity Metrics are Flawed
574. ICML 2025Position: Challenges and Future Directions of Data-Centric AI Alignment
575. ICML 2025Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
576. ICML 2025Position: Future Research and Challenges Remain Towards AI for Software Engineering
577. ICML 2025Position: Generative AI Regulation Can Learn from Social Media Regulation
578. ICML 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
579. ICML 2025Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process
580. ICML 2025Position: You Can't Manufacture a NeRF
581. ICML 2025Positional Attention: Expressivity and Learnability of Algorithmic Computation
582. ICML 2025Privacy Attacks on Image AutoRegressive Models
583. ICML 2025Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
584. ICML 2025Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers
585. ICML 2025Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
586. ICML 2025REG: Rectified Gradient Guidance for Conditional Diffusion Models
587. ICML 2025REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
588. ICML 2025Rényi Neural Processes
589. ICML 2025RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
590. ICML 2025Rethinking Aleatoric and Epistemic Uncertainty
591. ICML 2025Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
592. ICML 2025Revisiting Non-Acyclic GFlowNets in Discrete Environments
593. ICML 2025Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
594. ICML 2025RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
595. ICML 2025Robust Multi-bit Text Watermark with LLM-based Paraphrasers
596. ICML 2025Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability
597. ICML 2025RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression
598. ICML 2025S4S: Solving for a Fast Diffusion Model Solver
599. ICML 2025SADA: Stability-guided Adaptive Diffusion Acceleration
600. ICML 2025SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

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.