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

1,304 papers, page 19 of 27

901. ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
902. ICML 2024COPAL: Continual Pruning in Large Language Generative Models
903. ICML 2024Deep Stochastic Mechanics
904. ICML 2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
905. ICML 2024Demystifying SGD with Doubly Stochastic Gradients
906. ICML 2024DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
907. ICML 2024Differentially Private Sum-Product Networks
908. ICML 2024Diffusion Language Models Are Versatile Protein Learners
909. ICML 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
910. ICML 2024Disguised Copyright Infringement of Latent Diffusion Models
911. ICML 2024Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
912. ICML 2024Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
913. ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
914. ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
915. ICML 2024Evaluating Model Bias Requires Characterizing its Mistakes
916. ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
917. ICML 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
918. ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
919. ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller
920. ICML 2024FlowMM: Generating Materials with Riemannian Flow Matching
921. ICML 2024From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers
922. ICML 2024GenCO: Generating Diverse Designs with Combinatorial Constraints
923. ICML 2024Generative Active Learning for Long-tailed Instance Segmentation
924. ICML 2024Generative Conditional Distributions by Neural (Entropic) Optimal Transport
925. ICML 2024Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
926. ICML 2024Generative Marginalization Models
927. ICML 2024Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
928. ICML 2024Genie: Generative Interactive Environments
929. ICML 2024GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation
930. ICML 2024GiLOT: Interpreting Generative Language Models via Optimal Transport
931. ICML 2024Graph Geometry-Preserving Autoencoders
932. ICML 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
933. ICML 2024HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
934. ICML 2024High-Performance Temporal Reversible Spiking Neural Networks with $\mathcal{O}(L)$ Training Memory and $\mathcal{O}(1)$ Inference Cost
935. ICML 2024How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
936. ICML 2024How to Trace Latent Generative Model Generated Images without Artificial Watermark?
937. ICML 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
938. ICML 2024Image Hijacks: Adversarial Images can Control Generative Models at Runtime
939. ICML 2024Imitation Learning from Purified Demonstrations
940. ICML 2024Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
941. ICML 2024Indirectly Parameterized Concrete Autoencoders
942. ICML 2024Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
943. ICML 2024Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
944. ICML 2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
945. ICML 2024Language Models as Semantic Indexers
946. ICML 2024LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
947. ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
948. ICML 2024Learning to Infer Generative Template Programs for Visual Concepts
949. ICML 2024Learning to Model the World With Language
950. ICML 2024Learning Useful Representations of Recurrent Neural Network Weight Matrices

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.