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

4,314 papers, page 53 of 87

2601. ICLR 2024Transformer-VQ: Linear-Time Transformers via Vector Quantization
2602. ICLR 2024TUVF: Learning Generalizable Texture UV Radiance Fields
2603. ICLR 2024Unbiased Watermark for Large Language Models
2604. ICLR 2024Understanding the Effects of RLHF on LLM Generalisation and Diversity
2605. ICLR 2024Universal Guidance for Diffusion Models
2606. ICLR 2024Unveiling and Manipulating Prompt Influence in Large Language Models
2607. ICLR 2024Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
2608. ICLR 2024Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
2609. ICLR 2024WizardCoder: Empowering Code Large Language Models with Evol-Instruct
2610. ICLR 2024WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
2611. ICML 2024A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
2612. ICML 2024A Distributional Analogue to the Successor Representation
2613. ICML 2024A Fixed-Point Approach for Causal Generative Modeling
2614. ICML 2024A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
2615. ICML 2024Accelerated Speculative Sampling Based on Tree Monte Carlo
2616. ICML 2024Accelerating Convergence of Score-Based Diffusion Models, Provably
2617. ICML 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
2618. ICML 2024Adaptive Accompaniment with ReaLchords
2619. ICML 2024Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework
2620. ICML 2024Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
2621. ICML 2024AI Alignment with Changing and Influenceable Reward Functions
2622. ICML 2024An Interpretable Evaluation of Entropy-based Novelty of Generative Models
2623. ICML 2024Attribute Based Interpretable Evaluation Metrics for Generative Models
2624. ICML 2024Balanced Resonate-and-Fire Neurons
2625. ICML 2024Batch and match: black-box variational inference with a score-based divergence
2626. ICML 2024Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
2627. ICML 2024Bottleneck-Minimal Indexing for Generative Document Retrieval
2628. ICML 2024Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
2629. ICML 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
2630. ICML 2024Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
2631. ICML 2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
2632. ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
2633. ICML 2024COPAL: Continual Pruning in Large Language Generative Models
2634. ICML 2024Deep Stochastic Mechanics
2635. ICML 2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
2636. ICML 2024Demystifying SGD with Doubly Stochastic Gradients
2637. ICML 2024DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
2638. ICML 2024Differentially Private Sum-Product Networks
2639. ICML 2024Diffusion Language Models Are Versatile Protein Learners
2640. ICML 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
2641. ICML 2024Disguised Copyright Infringement of Latent Diffusion Models
2642. ICML 2024Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
2643. ICML 2024Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
2644. ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
2645. ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
2646. ICML 2024Evaluating Model Bias Requires Characterizing its Mistakes
2647. ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
2648. ICML 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
2649. ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
2650. ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller

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.