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 23 of 87

1101. NeurIPS 2026Analyzing Learning-Dynamics Across Loss and Prediction Choices for Diffusion Models
1102. NeurIPS 2026Architecture-Embedded Physics Priors for Mitigating Spectral Bias in Physics-Informed Neural Networks
1103. NeurIPS 2026Are we really tilting? The mechanics of reward guidance in flow and diffusion models
1104. NeurIPS 2026Auto-Rubric as Reward: From Implicit Preference to Explicit Generative Criteria
1105. NeurIPS 2026Behaving Better, Thinking Worse: Sycophancy Across Post-Training Stages
1106. NeurIPS 2026Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data
1107. NeurIPS 2026Beyond ICA: Identifiability by Symmetry Breaking
1108. NeurIPS 2026Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering
1109. NeurIPS 2026Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
1110. NeurIPS 2026Beyond Success Rates: Trainability and Extractability in Offline GCRL
1111. NeurIPS 2026BitDance: Scaling Autoregressive Generative Models with Binary Tokens
1112. NeurIPS 2026Black-box model classification under the discriminative factorization
1113. NeurIPS 2026Bottleneck Benchmark: Sequence Compression Under Controlled Difficulty and Fixed Latent Size
1114. NeurIPS 2026Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Trace
1115. NeurIPS 2026BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
1116. NeurIPS 2026Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
1117. NeurIPS 2026Bridge Graphical Models: Coupling, Projection, and Current-Preserving Dynamics for Generative Modeling
1118. NeurIPS 2026Bypassing PC1 Makes SAEs More Reproducible
1119. NeurIPS 2026Calibrating Generative Models to Feature Distributions with MMD Finetuning
1120. NeurIPS 2026Causal Bias Detection in Generative Artifical Intelligence
1121. NeurIPS 2026Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
1122. NeurIPS 2026Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machine
1123. NeurIPS 2026ChiP-STAR: Spatial-Topological Attention for Pre-trained Generative Chip Routing
1124. NeurIPS 2026Classification at the Edge of Stability: Unifying Self-Stabilization and Convergence Rates
1125. NeurIPS 2026Closed-Loop Alignment: Socially Coupled In-Context Learning and Relational Posterior Collapse
1126. NeurIPS 2026Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes
1127. NeurIPS 2026Collapse Hunter: Tackling the Dimensional Degeneration in Generative Ranking
1128. NeurIPS 2026Comparing Transformers and Hybrid Models at the Token Level
1129. NeurIPS 2026Concept frustration: Aligning human concepts and machine representations
1130. NeurIPS 2026Conformal Cache: Reliable Proxy-Discrepancy Caching for Fast Generative Inference
1131. NeurIPS 2026Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
1132. NeurIPS 2026Constrained Look-ahead Guidance for Interference-Aware Flow Editing
1133. NeurIPS 2026Contact Geometry for Generative Models: An Unbalanced Optimal Transport Formulation
1134. NeurIPS 2026Continuous Personalized Diffusion Model via Spinor-Component Forward Geometry
1135. NeurIPS 2026Continuously-Augmented Hybrid Masked Diffusion Model for Data Imputation
1136. NeurIPS 2026Controllable Molecular Generative Foundation Models
1137. NeurIPS 2026Coreset-Induced Conditional Velocity Flow Matching
1138. NeurIPS 2026Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR
1139. NeurIPS 2026CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
1140. NeurIPS 2026Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models
1141. NeurIPS 2026D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform
1142. NeurIPS 2026Decomposed Graded Verifier for Generative World Modeling
1143. NeurIPS 2026Decomposing Conformal Uncertainty: Calibration- and Instance-Driven Feature Attribution
1144. NeurIPS 2026Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
1145. NeurIPS 2026Deep Minds and Shallow Probes
1146. NeurIPS 2026Denoising Time Matters: Diverse Generation in Diffusion Language Models
1147. NeurIPS 2026DiffATS: Diffusion in Aligned Tensor Space
1148. NeurIPS 2026Differentiable Knapsack and Top-k Operators via Dynamic Programming
1149. NeurIPS 2026Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
1150. NeurIPS 2026DiLaDiff: Distilled Latent-augmented Diffusion for Language Modeling

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.