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 15 of 16

701. NeurIPS 2024Simple and Fast Distillation of Diffusion Models
702. NeurIPS 2024Simplified and Generalized Masked Diffusion for Discrete Data
703. NeurIPS 2024Skinned Motion Retargeting with Dense Geometric Interaction Perception
704. NeurIPS 2024SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction
705. NeurIPS 2024Soft ascent-descent as a stable and flexible alternative to flooding
706. NeurIPS 2024Sparse Bayesian Generative Modeling for Compressive Sensing
707. NeurIPS 2024Sparse High Rank Adapters
708. NeurIPS 2024Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
709. NeurIPS 2024SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models
710. NeurIPS 2024Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
711. NeurIPS 2024Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness
712. NeurIPS 2024Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
713. NeurIPS 2024Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
714. NeurIPS 2024Statistical Efficiency of Distributional Temporal Difference Learning
715. NeurIPS 2024Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
716. NeurIPS 2024Studying How to Efficiently and Effectively Guide Models with Explanations - A Reproducibility Study
717. NeurIPS 2024Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass
718. NeurIPS 2024SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
719. NeurIPS 2024Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
720. NeurIPS 2024Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
721. NeurIPS 2024Taming Generative Diffusion Prior for Universal Blind Image Restoration
722. NeurIPS 2024Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
723. NeurIPS 2024Tetrahedron Splatting for 3D Generation
724. NeurIPS 2024The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
725. NeurIPS 2024The GAN is dead; long live the GAN! A Modern GAN Baseline
726. NeurIPS 2024The tree autoencoder model, with application to hierarchical data visualization
727. NeurIPS 2024Theoretical guarantees in KL for Diffusion Flow Matching
728. NeurIPS 2024Thought of Search: Planning with Language Models Through The Lens of Efficiency
729. NeurIPS 2024Time-Reversal Provides Unsupervised Feedback to LLMs
730. NeurIPS 2024Towards a Scalable Reference-Free Evaluation of Generative Models
731. NeurIPS 2024Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
732. NeurIPS 2024Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
733. NeurIPS 2024Training Data Attribution via Approximate Unrolling
734. NeurIPS 2024TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks
735. NeurIPS 2024Transcendence: Generative Models Can Outperform The Experts That Train Them
736. NeurIPS 2024Transcoders find interpretable LLM feature circuits
737. NeurIPS 2024Transferring disentangled representations: bridging the gap between synthetic and real images
738. NeurIPS 2024Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
739. NeurIPS 2024Transformer Doctor: Diagnosing and Treating Vision Transformers
740. NeurIPS 2024Transformers Can Do Arithmetic with the Right Embeddings
741. NeurIPS 2024Transformers on Markov data: Constant depth suffices
742. NeurIPS 2024Truncated Variance Reduced Value Iteration
743. NeurIPS 2024TSDS: Data Selection for Task-Specific Model Finetuning
744. NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
745. NeurIPS 2024Understanding Hallucinations in Diffusion Models through Mode Interpolation
746. NeurIPS 2024Understanding Information Storage and Transfer in Multi-Modal Large Language Models
747. NeurIPS 2024UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
748. NeurIPS 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
749. NeurIPS 2024Unity by Diversity: Improved Representation Learning for Multimodal VAEs
750. NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent 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.