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

768 papers, page 14 of 16

651. NeurIPS 2024PaCE: Parsimonious Concept Engineering for Large Language Models
652. NeurIPS 2024PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
653. NeurIPS 2024Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
654. NeurIPS 2024Parametric model reduction of mean-field and stochastic systems via higher-order action matching
655. NeurIPS 2024Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
656. NeurIPS 2024PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision
657. NeurIPS 2024PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
658. NeurIPS 2024PERIA: Perceive, Reason, Imagine, Act via Holistic Language and Vision Planning for Manipulation
659. NeurIPS 2024Pessimistic Backward Policy for GFlowNets
660. NeurIPS 2024Phased Consistency Models
661. NeurIPS 2024PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
662. NeurIPS 2024Piecewise deterministic generative models
663. NeurIPS 2024Poisson Variational Autoencoder
664. NeurIPS 2024Polyhedral Complex Derivation from Piecewise Trilinear Networks
665. NeurIPS 2024Practical Shuffle Coding
666. NeurIPS 2024Pricing and Competition for Generative AI
667. NeurIPS 2024ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
668. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
669. NeurIPS 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
670. NeurIPS 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
671. NeurIPS 2024PTQ4DiT: Post-training Quantization for Diffusion Transformers
672. NeurIPS 2024PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
673. NeurIPS 2024QGFN: Controllable Greediness with Action Values
674. NeurIPS 2024QTIP: Quantization with Trellises and Incoherence Processing
675. NeurIPS 2024Questioning the Survey Responses of Large Language Models
676. NeurIPS 2024Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
677. NeurIPS 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
678. NeurIPS 2024Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
679. NeurIPS 2024RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
680. NeurIPS 2024Rethinking Score Distillation as a Bridge Between Image Distributions
681. NeurIPS 2024Retrieval-Augmented Diffusion Models for Time Series Forecasting
682. NeurIPS 2024Return of Unconditional Generation: A Self-supervised Representation Generation Method
683. NeurIPS 2024Right this way: Can VLMs Guide Us to See More to Answer Questions?
684. NeurIPS 2024RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts
685. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
686. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
687. NeurIPS 2024Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
688. NeurIPS 2024Score-based generative models are provably robust: an uncertainty quantification perspective
689. NeurIPS 2024SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
690. NeurIPS 2024SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers
691. NeurIPS 2024Search for Efficient Large Language Models
692. NeurIPS 2024Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
693. NeurIPS 2024Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
694. NeurIPS 2024Self-Labeling the Job Shop Scheduling Problem
695. NeurIPS 2024Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation
696. NeurIPS 2024Self-playing Adversarial Language Game Enhances LLM Reasoning
697. NeurIPS 2024SelfCodeAlign: Self-Alignment for Code Generation
698. NeurIPS 2024SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
699. NeurIPS 2024Sequoia: Scalable and Robust Speculative Decoding
700. NeurIPS 2024Simple and Effective Masked Diffusion Language 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.