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 13 of 16

601. NeurIPS 2024Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models
602. NeurIPS 2024LG-VQ: Language-Guided Codebook Learning
603. NeurIPS 2024Light Unbalanced Optimal Transport
604. NeurIPS 2024Linguistic Collapse: Neural Collapse in (Large) Language Models
605. NeurIPS 2024LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
606. NeurIPS 2024LLM Circuit Analyses Are Consistent Across Training and Scale
607. NeurIPS 2024Local Curvature Smoothing with Stein's Identity for Efficient Score Matching
608. NeurIPS 2024Local to Global: Learning Dynamics and Effect of Initialization for Transformers
609. NeurIPS 2024Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild
610. NeurIPS 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
611. NeurIPS 2024Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT
612. NeurIPS 2024MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models
613. NeurIPS 2024MassSpecGym: A benchmark for the discovery and identification of molecules
614. NeurIPS 2024Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
615. NeurIPS 2024Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs
616. NeurIPS 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
617. NeurIPS 2024Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
618. NeurIPS 2024Metric Flow Matching for Smooth Interpolations on the Data Manifold
619. NeurIPS 2024Metric Space Magnitude for Evaluating the Diversity of Latent Representations
620. NeurIPS 2024MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs
621. NeurIPS 2024Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes
622. NeurIPS 2024MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
623. NeurIPS 2024Molecule Design by Latent Prompt Transformer
624. NeurIPS 2024MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
625. NeurIPS 2024MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
626. NeurIPS 2024MotionCraft: Physics-Based Zero-Shot Video Generation
627. NeurIPS 2024MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
628. NeurIPS 2024Multimodal Large Language Models Make Text-to-Image Generative Models Align Better
629. NeurIPS 2024Multistep Distillation of Diffusion Models via Moment Matching
630. NeurIPS 2024MVGamba: Unify 3D Content Generation as State Space Sequence Modeling
631. NeurIPS 2024MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing
632. NeurIPS 2024MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
633. NeurIPS 2024Nature-Inspired Local Propagation
634. NeurIPS 2024Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
635. NeurIPS 2024Nearest Neighbor Speculative Decoding for LLM Generation and Attribution
636. NeurIPS 2024Neural Cover Selection for Image Steganography
637. NeurIPS 2024Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
638. NeurIPS 2024Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses
639. NeurIPS 2024OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
640. NeurIPS 2024On Divergence Measures for Training GFlowNets
641. NeurIPS 2024On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
642. NeurIPS 2024On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
643. NeurIPS 2024On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
644. NeurIPS 2024On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
645. NeurIPS 2024On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
646. NeurIPS 2024On the Noise Robustness of In-Context Learning for Text Generation
647. NeurIPS 2024One-Shot Safety Alignment for Large Language Models via Optimal Dualization
648. NeurIPS 2024Optimal Flow Matching: Learning Straight Trajectories in Just One Step
649. NeurIPS 2024Optimal Multiclass U-Calibration Error and Beyond
650. NeurIPS 2024Order-Independence Without Fine Tuning

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.