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 14 of 25

651. NeurIPS 2023DropCompute: simple and more robust distributed synchronous training via compute variance reduction
652. NeurIPS 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
653. NeurIPS 2023Dynamic Pricing and Learning with Bayesian Persuasion
654. NeurIPS 2023Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
655. NeurIPS 2023Dynamically Masked Discriminator for GANs
656. NeurIPS 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
657. NeurIPS 2023Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
658. NeurIPS 2023EDGI: Equivariant Diffusion for Planning with Embodied Agents
659. NeurIPS 2023Effective Targeted Attacks for Adversarial Self-Supervised Learning
660. NeurIPS 2023Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning
661. NeurIPS 2023Efficient Beam Tree Recursion
662. NeurIPS 2023Efficient Equivariant Transfer Learning from Pretrained Models
663. NeurIPS 2023Efficient Exploration in Continuous-time Model-based Reinforcement Learning
664. NeurIPS 2023Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
665. NeurIPS 2023Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
666. NeurIPS 2023Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
667. NeurIPS 2023Efficient Training of Energy-Based Models Using Jarzynski Equality
668. NeurIPS 2023Efficiently incorporating quintuple interactions into geometric deep learning force fields
669. NeurIPS 2023EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
670. NeurIPS 2023Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
671. NeurIPS 2023Emergent and Predictable Memorization in Large Language Models
672. NeurIPS 2023Encoding Human Behavior in Information Design through Deep Learning
673. NeurIPS 2023Energy Transformer
674. NeurIPS 2023Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
675. NeurIPS 2023Enhancing Adversarial Robustness via Score-Based Optimization
676. NeurIPS 2023Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
677. NeurIPS 2023Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift
678. NeurIPS 2023Entropy-based Training Methods for Scalable Neural Implicit Samplers
679. NeurIPS 2023Episodic Multi-Task Learning with Heterogeneous Neural Processes
680. NeurIPS 2023Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation
681. NeurIPS 2023Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
682. NeurIPS 2023Equivariant Single View Pose Prediction Via Induced and Restriction Representations
683. NeurIPS 2023Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations
684. NeurIPS 2023Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
685. NeurIPS 2023Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
686. NeurIPS 2023Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance
687. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
688. NeurIPS 2023Evaluating and Inducing Personality in Pre-trained Language Models
689. NeurIPS 2023Evaluating Neuron Interpretation Methods of NLP Models
690. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
691. NeurIPS 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
692. NeurIPS 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
693. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
694. NeurIPS 2023Expert load matters: operating networks at high accuracy and low manual effort
695. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
696. NeurIPS 2023Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
697. NeurIPS 2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
698. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
699. NeurIPS 2023Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
700. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning

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.