深度学习 · generative models and autoencoders论文趋势 2023–2026

主要结论

• 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
• NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · generative models and autoencoders论文

共 776 篇，第 11 / 16 页

501. NeurIPS 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
502. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
503. NeurIPS 2023Optimal Transport Model Distributional Robustness
504. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
505. NeurIPS 2023Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
506. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
507. NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
508. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
509. NeurIPS 2023PaintSeg: Painting Pixels for Training-free Segmentation
510. NeurIPS 2023Pairwise GUI Dataset Construction Between Android Phones and Tablets
511. NeurIPS 2023Parallel Sampling of Diffusion Models
512. NeurIPS 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
513. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
514. NeurIPS 2023Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
515. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
516. NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
517. NeurIPS 2023Passive learning of active causal strategies in agents and language models
518. NeurIPS 2023Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
519. NeurIPS 2023Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
520. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
521. NeurIPS 2023Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
522. NeurIPS 2023Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
523. NeurIPS 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
524. NeurIPS 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
525. NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
526. NeurIPS 2023Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
527. NeurIPS 2023PoET: A generative model of protein families as sequences-of-sequences
528. NeurIPS 2023PointGPT: Auto-regressively Generative Pre-training from Point Clouds
529. NeurIPS 2023PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
530. NeurIPS 2023Polyhedron Attention Module: Learning Adaptive-order Interactions
531. NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
532. NeurIPS 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
533. NeurIPS 2023Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
534. NeurIPS 2023Predicting a Protein's Stability under a Million Mutations
535. NeurIPS 2023PreDiff: Precipitation Nowcasting with Latent Diffusion Models
536. NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
537. NeurIPS 2023Private estimation algorithms for stochastic block models and mixture models
538. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
539. NeurIPS 2023PrObeD: Proactive Object Detection Wrapper
540. NeurIPS 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
541. NeurIPS 2023Promises and Pitfalls of Threshold-based Auto-labeling
542. NeurIPS 2023Prompt-augmented Temporal Point Process for Streaming Event Sequence
543. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
544. NeurIPS 2023ProPILE: Probing Privacy Leakage in Large Language Models
545. NeurIPS 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
546. NeurIPS 2023ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
547. NeurIPS 2023ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
548. NeurIPS 2023ProteinShake: Building datasets and benchmarks for deep learning on protein structures
549. NeurIPS 2023Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior
550. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？

共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。

NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？

NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。

ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？

CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。

哪个会议的深度学习 · generative models and autoencoders论文最多？

NeurIPS，2023–2026 年共 1,714 篇。