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 1,216 篇，第 19 / 25 页

901. NeurIPS 2023Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
902. NeurIPS 2023Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
903. NeurIPS 2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
904. NeurIPS 2023Neural Sampling in Hierarchical Exponential-family Energy-based Models
905. NeurIPS 2023NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
906. NeurIPS 2023Neuro-symbolic Learning Yielding Logical Constraints
907. NeurIPS 2023NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
908. NeurIPS 2023No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
909. NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
910. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
911. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
912. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
913. NeurIPS 2023Nonparametric Boundary Geometry in Physics Informed Deep Learning
914. NeurIPS 2023Normalization-Equivariant Neural Networks with Application to Image Denoising
915. NeurIPS 2023NPCL: Neural Processes for Uncertainty-Aware Continual Learning
916. NeurIPS 2023Object-centric Learning with Cyclic Walks between Parts and Whole
917. NeurIPS 2023Object-Centric Slot Diffusion
918. NeurIPS 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
919. NeurIPS 2023On Calibrating Diffusion Probabilistic Models
920. NeurIPS 2023On Learning Latent Models with Multi-Instance Weak Supervision
921. NeurIPS 2023On Measuring Fairness in Generative Models
922. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
923. NeurIPS 2023On quantum backpropagation, information reuse, and cheating measurement collapse
924. NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
925. NeurIPS 2023On Single-Index Models beyond Gaussian Data
926. NeurIPS 2023On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
927. NeurIPS 2023On the Constrained Time-Series Generation Problem
928. NeurIPS 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
929. NeurIPS 2023On the impact of activation and normalization in obtaining isometric embeddings at initialization
930. NeurIPS 2023On the Role of Entanglement and Statistics in Learning
931. NeurIPS 2023On the Role of Randomization in Adversarially Robust Classification
932. NeurIPS 2023On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
933. NeurIPS 2023On the Variance, Admissibility, and Stability of Empirical Risk Minimization
934. NeurIPS 2023On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models
935. NeurIPS 2023One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
936. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
937. NeurIPS 2023One-Step Diffusion Distillation via Deep Equilibrium Models
938. NeurIPS 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
939. NeurIPS 2023Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
940. NeurIPS 2023Online Clustering of Bandits with Misspecified User Models
941. NeurIPS 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
942. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
943. NeurIPS 2023Optimal Transport Model Distributional Robustness
944. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
945. NeurIPS 2023Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
946. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
947. NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
948. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
949. NeurIPS 2023PaintSeg: Painting Pixels for Training-free Segmentation
950. NeurIPS 2023Pairwise GUI Dataset Construction Between Android Phones and Tablets

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。