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 12 of 25

551. NeurIPS 2023Characterizing Out-of-Distribution Error via Optimal Transport
552. NeurIPS 2023Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models
553. NeurIPS 2023Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark
554. NeurIPS 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
555. NeurIPS 2023ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
556. NeurIPS 2023Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits
557. NeurIPS 2023Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
558. NeurIPS 2023Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
559. NeurIPS 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
560. NeurIPS 2023Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
561. NeurIPS 2023Collaborative Learning via Prediction Consensus
562. NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
563. NeurIPS 2023CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
564. NeurIPS 2023Combinatorial Group Testing with Selfish Agents
565. NeurIPS 2023Combining Behaviors with the Successor Features Keyboard
566. NeurIPS 2023Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems
567. NeurIPS 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
568. NeurIPS 2023Complexity Matters: Rethinking the Latent Space for Generative Modeling
569. NeurIPS 2023Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
570. NeurIPS 2023Compositional Generalization from First Principles
571. NeurIPS 2023Compositional Sculpting of Iterative Generative Processes
572. NeurIPS 2023Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
573. NeurIPS 2023Concept Algebra for (Score-Based) Text-Controlled Generative Models
574. NeurIPS 2023Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
575. NeurIPS 2023Conditional independence testing under misspecified inductive biases
576. NeurIPS 2023Conditional score-based diffusion models for Bayesian inference in infinite dimensions
577. NeurIPS 2023Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
578. NeurIPS 2023Connecting Pre-trained Language Model and Downstream Task via Properties of Representation
579. NeurIPS 2023Conservative State Value Estimation for Offline Reinforcement Learning
580. NeurIPS 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
581. NeurIPS 2023Contextual Bandits and Imitation Learning with Preference-Based Active Queries
582. NeurIPS 2023Contextually Affinitive Neighborhood Refinery for Deep Clustering
583. NeurIPS 2023Continuous-Time Functional Diffusion Processes
584. NeurIPS 2023Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time
585. NeurIPS 2023Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
586. NeurIPS 2023Contrastive Sampling Chains in Diffusion Models
587. NeurIPS 2023Contrastive Training of Complex-Valued Autoencoders for Object Discovery
588. NeurIPS 2023Convolutional Neural Operators for robust and accurate learning of PDEs
589. NeurIPS 2023Coop: Memory is not a Commodity
590. NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
591. NeurIPS 2023Counterfactual Generation with Identifiability Guarantees
592. NeurIPS 2023Counterfactually Fair Representation
593. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
594. NeurIPS 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
595. NeurIPS 2023CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
596. NeurIPS 2023CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation
597. NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
598. NeurIPS 2023Curvature Filtrations for Graph Generative Model Evaluation
599. NeurIPS 2023Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
600. NeurIPS 2023DAC-DETR: Divide the Attention Layers and Conquer

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.