An Integrated Deep Learning and Statistical Framework for Whole-Network Gene--Environment Association with Leaf Vascular Architecture

arXiv:2607.22763 · cs.LG, stat.ML · Submitted 2026-07-23 · Read on arXiv

Geran Zhao, Yangsheng Wang, Xiaotian Dai, Guifang Fu

cs.LG, stat.ML

Submitted: 2026-07-23

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 31× in this paper · explained in 2 episodes
• Transformer Encoder — 8× in this paper · explained in 2 episodes
• Copula — 6× in this paper · explained in 2 episodes
• Decoder — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Feature Fusion Module — 4× in this paper
• Semiparametric Sparse Canonical Correlation Analysis — 4× in this paper
• Transformers — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Residual Connections — 3× in this paper · explained in 3 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Batch Normalization — 1× in this paper
• Bayesian Information Criterion — 1× in this paper
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Convolutional Neural Network (CNN) — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 6 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Lasso — 1× in this paper · explained in 2 episodes
• Multi-Layer Perceptron — 1× in this paper
• Multi-head Self-Attention — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 8 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Two-Stage Architecture — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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