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

1,216 papers, page 20 of 25

951. NeurIPS 2023Parallel Sampling of Diffusion Models
952. NeurIPS 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
953. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
954. NeurIPS 2023Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
955. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
956. NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
957. NeurIPS 2023Passive learning of active causal strategies in agents and language models
958. NeurIPS 2023Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
959. NeurIPS 2023Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
960. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
961. NeurIPS 2023Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
962. NeurIPS 2023Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
963. NeurIPS 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
964. NeurIPS 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
965. NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
966. NeurIPS 2023Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
967. NeurIPS 2023PoET: A generative model of protein families as sequences-of-sequences
968. NeurIPS 2023PointGPT: Auto-regressively Generative Pre-training from Point Clouds
969. NeurIPS 2023PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
970. NeurIPS 2023Polyhedron Attention Module: Learning Adaptive-order Interactions
971. NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
972. NeurIPS 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
973. NeurIPS 2023Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
974. NeurIPS 2023Predicting a Protein's Stability under a Million Mutations
975. NeurIPS 2023PreDiff: Precipitation Nowcasting with Latent Diffusion Models
976. NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
977. NeurIPS 2023Private estimation algorithms for stochastic block models and mixture models
978. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
979. NeurIPS 2023PrObeD: Proactive Object Detection Wrapper
980. NeurIPS 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
981. NeurIPS 2023Promises and Pitfalls of Threshold-based Auto-labeling
982. NeurIPS 2023Prompt-augmented Temporal Point Process for Streaming Event Sequence
983. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
984. NeurIPS 2023ProPILE: Probing Privacy Leakage in Large Language Models
985. NeurIPS 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
986. NeurIPS 2023ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
987. NeurIPS 2023ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
988. NeurIPS 2023ProteinShake: Building datasets and benchmarks for deep learning on protein structures
989. NeurIPS 2023Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior
990. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning
991. NeurIPS 2023Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
992. NeurIPS 2023Provably (More) Sample-Efficient Offline RL with Options
993. NeurIPS 2023Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
994. NeurIPS 2023PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
995. NeurIPS 2023PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
996. NeurIPS 2023Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models
997. NeurIPS 2023QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data
998. NeurIPS 2023Quantification of Uncertainty with Adversarial Models
999. NeurIPS 2023Quantifying the Cost of Learning in Queueing Systems
1000. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution

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.