Deep learning · generative models and autoencoders papers, 2023–2026

Deep learning area: generative_models_and_autoencoders

Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 4,314 papers are tagged with this topic.

Key findings

Papers per year

Papers per venue

Counts by year and venue

Deep learning · generative models and autoencoders papers by year and venue
YearNeurIPSICLRICMLCVPRTotal
202377678286761,216
2024349174139106768
2025240275275110900
20263493036041741,430

Representative papers

Spread across years and venues so every edition shows up

All Deep learning · generative models and autoencoders papers

1,430 papers, page 20 of 29

  1. ICML 2026Semantic Granularity Navigation in Image Editing
  2. ICML 2026Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression
  3. ICML 2026Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
  4. ICML 2026SemanticNVS: Improving Semantic Scene Understanding in Generative Novel View Synthesis
  5. ICML 2026Set Diffusion: Interpolating Token Orderings between Autoregression and Diffusion for Fast and Flexible Decoding
  6. ICML 2026SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
  7. ICML 2026Sharper Generalization Guarantees for Asynchronous SGD: Beyond Lipschitzness, Smoothness and Data Homogeneity
  8. ICML 2026Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
  9. ICML 2026Simple Denoising Diffusion Language Models
  10. ICML 2026SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation
  11. ICML 2026Skipping the Zeros in Diffusion Models for Sparse Data Generation
  12. ICML 2026SlerpFlow: Spherical Trajectory Correction for Rectified Flow Inversion
  13. ICML 2026SMART: Scalable Mesh‑free Aerodynamic Simulations from Raw Geometries using a Transformer‑based Surrogate Model
  14. ICML 2026Solving Inverse Problems with Flow-based Models via Model Predictive Control
  15. ICML 2026Sparse Autoencoders are Topic Models
  16. ICML 2026Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech
  17. ICML 2026Sparse Bayesian Deep Functional Learning with Structured Region Selection
  18. ICML 2026Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
  19. ICML 2026Spatiotemporal Imputation with Graph-Informed Flow Matching
  20. ICML 2026SpecPL: Disentangling Spectral Granularity for Prompt Learning
  21. ICML 2026Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
  22. ICML 2026Spectral Guidance for Flexible and Efficient Control of Diffusion Models
  23. ICML 2026Spectrally-Guided Diffusion Noise Schedules
  24. ICML 2026Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
  25. ICML 2026SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models
  26. ICML 2026Stabilizing Reinforcement Learning for Diffusion Language Models
  27. ICML 2026Stable Velocity: A Variance Perspective on Flow Matching
  28. ICML 2026Statistical Consistency and Generalization of Contrastive Representation Learning
  29. ICML 2026Steer Like the LLM: Activation Steering that Mimics Prompting
  30. ICML 2026Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
  31. ICML 2026Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
  32. ICML 2026Strategic Candidacy in Generative AI Arenas
  33. ICML 2026StructMAR: Structure-Aware Masked Autoregression for Explicit Layout Alignment in Text-to-Image Generation
  34. ICML 2026Structurally Aligned Subtask-Level Memory for Software Engineering Agents
  35. ICML 2026Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
  36. ICML 2026Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
  37. ICML 2026SVRG and Beyond via Posterior Correction
  38. ICML 2026Symbol-Equivariant Recurrent Reasoning Models
  39. ICML 2026SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
  40. ICML 2026SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
  41. ICML 2026TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling
  42. ICML 2026Tail Annealing for Heavy-Tailed Flow Matching
  43. ICML 2026Taming Aleatoric Impulse in Off-Policy Reinforcement Learning
  44. ICML 2026Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
  45. ICML 2026Temperature Scaling in Discrete Sequence (Language) Models
  46. ICML 2026Temporal Difference Learning for Diffusion Models
  47. ICML 2026Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
  48. ICML 2026Test-Time Anchoring for Discrete Diffusion Posterior Sampling
  49. ICML 2026Test-Time Guidance for Flow-Based Generative Models via Parallel Tempering on Source Distributions
  50. ICML 2026Test-Time Reinforcement Learning for Flow Matching

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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.

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