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 15 / 16 页

701. NeurIPS 2023The Quantization Model of Neural Scaling
702. NeurIPS 2023The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
703. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
704. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
705. NeurIPS 2023Theoretical and Practical Perspectives on what Influence Functions Do
706. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
707. NeurIPS 2023Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
708. NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
709. NeurIPS 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
710. NeurIPS 2023Tools for Verifying Neural Models' Training Data
711. NeurIPS 2023Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification
712. NeurIPS 2023Topological Obstructions and How to Avoid Them
713. NeurIPS 2023Topological Parallax: A Geometric Specification for Deep Perception Models
714. NeurIPS 2023TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
715. NeurIPS 2023Toward Understanding Generative Data Augmentation
716. NeurIPS 2023Towards a Unified Framework of Contrastive Learning for Disentangled Representations
717. NeurIPS 2023Towards Automated Circuit Discovery for Mechanistic Interpretability
718. NeurIPS 2023Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
719. NeurIPS 2023Towards Free Data Selection with General-Purpose Models
720. NeurIPS 2023Towards robust and generalizable representations of extracellular data using contrastive learning
721. NeurIPS 2023Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
722. NeurIPS 2023Towards Test-Time Refusals via Concept Negation
723. NeurIPS 2023Towards Unbounded Machine Unlearning
724. NeurIPS 2023Trade-off Between Efficiency and Consistency for Removal-based Explanations
725. NeurIPS 2023Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
726. NeurIPS 2023Training Energy-Based Normalizing Flow with Score-Matching Objectives
727. NeurIPS 2023Training Private Models That Know What They Don’t Know
728. NeurIPS 2023Training Transitive and Commutative Multimodal Transformers with LoReTTa
729. NeurIPS 2023Trans-Dimensional Generative Modeling via Jump Diffusion Models
730. NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
731. NeurIPS 2023Transformer-based Planning for Symbolic Regression
732. NeurIPS 2023Tree Variational Autoencoders
733. NeurIPS 2023Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
734. NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
735. NeurIPS 2023TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
736. NeurIPS 2023Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
737. NeurIPS 2023Unbiased learning of deep generative models with structured discrete representations
738. NeurIPS 2023Uncertainty Quantification via Neural Posterior Principal Components
739. NeurIPS 2023Uncertainty-Aware Instance Reweighting for Off-Policy Learning
740. NeurIPS 2023Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
741. NeurIPS 2023Understanding and Improving Feature Learning for Out-of-Distribution Generalization
742. NeurIPS 2023Understanding and Mitigating Copying in Diffusion Models
743. NeurIPS 2023Understanding Contrastive Learning via Distributionally Robust Optimization
744. NeurIPS 2023Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes
745. NeurIPS 2023Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
746. NeurIPS 2023Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
747. NeurIPS 2023Undirected Probabilistic Model for Tensor Decomposition
748. NeurIPS 2023Unifying GANs and Score-Based Diffusion as Generative Particle Models
749. NeurIPS 2023Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
750. NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures

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。