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

139 papers, page 1 of 3

1. ICML 2024A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
2. ICML 2024A Distributional Analogue to the Successor Representation
3. ICML 2024A Fixed-Point Approach for Causal Generative Modeling
4. ICML 2024A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
5. ICML 2024Accelerated Speculative Sampling Based on Tree Monte Carlo
6. ICML 2024Accelerating Convergence of Score-Based Diffusion Models, Provably
7. ICML 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
8. ICML 2024Adaptive Accompaniment with ReaLchords
9. ICML 2024Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework
10. ICML 2024Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
11. ICML 2024AI Alignment with Changing and Influenceable Reward Functions
12. ICML 2024An Interpretable Evaluation of Entropy-based Novelty of Generative Models
13. ICML 2024Attribute Based Interpretable Evaluation Metrics for Generative Models
14. ICML 2024Balanced Resonate-and-Fire Neurons
15. ICML 2024Batch and match: black-box variational inference with a score-based divergence
16. ICML 2024Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
17. ICML 2024Bottleneck-Minimal Indexing for Generative Document Retrieval
18. ICML 2024Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
19. ICML 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
20. ICML 2024Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
21. ICML 2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
22. ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
23. ICML 2024COPAL: Continual Pruning in Large Language Generative Models
24. ICML 2024Deep Stochastic Mechanics
25. ICML 2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
26. ICML 2024Demystifying SGD with Doubly Stochastic Gradients
27. ICML 2024DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
28. ICML 2024Differentially Private Sum-Product Networks
29. ICML 2024Diffusion Language Models Are Versatile Protein Learners
30. ICML 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
31. ICML 2024Disguised Copyright Infringement of Latent Diffusion Models
32. ICML 2024Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
33. ICML 2024Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
34. ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
35. ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
36. ICML 2024Evaluating Model Bias Requires Characterizing its Mistakes
37. ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
38. ICML 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
39. ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
40. ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller
41. ICML 2024FlowMM: Generating Materials with Riemannian Flow Matching
42. ICML 2024From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers
43. ICML 2024GenCO: Generating Diverse Designs with Combinatorial Constraints
44. ICML 2024Generative Active Learning for Long-tailed Instance Segmentation
45. ICML 2024Generative Conditional Distributions by Neural (Entropic) Optimal Transport
46. ICML 2024Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
47. ICML 2024Generative Marginalization Models
48. ICML 2024Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
49. ICML 2024Genie: Generative Interactive Environments
50. ICML 2024GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation

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.