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

1,714 papers, page 33 of 35

1601. NeurIPS 2023STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
1602. NeurIPS 2023Strategic Apple Tasting
1603. NeurIPS 2023STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
1604. NeurIPS 2023Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
1605. NeurIPS 2023StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
1606. NeurIPS 2023Structural Pruning for Diffusion Models
1607. NeurIPS 2023Structured Federated Learning through Clustered Additive Modeling
1608. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
1609. NeurIPS 2023Structured State Space Models for In-Context Reinforcement Learning
1610. NeurIPS 2023StyleGAN knows Normal, Depth, Albedo, and More
1611. NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
1612. NeurIPS 2023SwiFT: Swin 4D fMRI Transformer
1613. NeurIPS 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
1614. NeurIPS 2023Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
1615. NeurIPS 2023Synthetic Experience Replay
1616. NeurIPS 2023T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
1617. NeurIPS 2023TabMT: Generating tabular data with masked transformers
1618. NeurIPS 2023Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system
1619. NeurIPS 2023TART: A plug-and-play Transformer module for task-agnostic reasoning
1620. NeurIPS 2023Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
1621. NeurIPS 2023Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
1622. NeurIPS 2023Task-aware world model learning with meta weighting via bi-level optimization
1623. NeurIPS 2023TaskMet: Task-driven Metric Learning for Model Learning
1624. NeurIPS 2023Taylor TD-learning
1625. NeurIPS 2023Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
1626. NeurIPS 2023Temporal Dynamic Quantization for Diffusion Models
1627. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
1628. NeurIPS 2023Tester-Learners for Halfspaces: Universal Algorithms
1629. NeurIPS 2023Text-to-Image Diffusion Models are Zero Shot Classifiers
1630. NeurIPS 2023Textually Pretrained Speech Language Models
1631. NeurIPS 2023The Adversarial Consistency of Surrogate Risks for Binary Classification
1632. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
1633. NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
1634. NeurIPS 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
1635. NeurIPS 2023The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
1636. NeurIPS 2023The geometry of hidden representations of large transformer models
1637. NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
1638. NeurIPS 2023The probability flow ODE is provably fast
1639. NeurIPS 2023The Quantization Model of Neural Scaling
1640. NeurIPS 2023The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
1641. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
1642. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
1643. NeurIPS 2023Theoretical and Practical Perspectives on what Influence Functions Do
1644. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
1645. NeurIPS 2023Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
1646. NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
1647. NeurIPS 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
1648. NeurIPS 2023Tools for Verifying Neural Models' Training Data
1649. NeurIPS 2023Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification
1650. NeurIPS 2023Topological Obstructions and How to Avoid Them

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.