Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

Artificial Intelligence (AI)Algorithm Attention Autoencoder AGI / Artificial General Intelligence

B

BERT Boosting Backpropagation Batch Normalization Bias

C

Classifier / Classification Cross-Validation Clustering CNN / Convolutional Neural Network Chatbot

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

Foundation Model Forward Propagation Fusion / Multimodal Fusion Feature Extraction Fine-tuning

G

GAN / Generative Adversarial Network Grounding Generative AI Gradient Descent Graph Neural Network (GNN)

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

Interpretability Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)Knowledge Distillation K-means Clustering

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Latent Variable Loss Function Learning Rate

M

Multimodal / Multimodality Multi-head Attention Machine Learning (ML)Model Meta-learning

N

NLU / Natural Language Understanding Normalization Neural Network Novelty Detection / Anomaly Detection NLP / Natural Language Processing

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

RNN / Recurrent Neural Network Representation Learning Retrieval Augmented Generation (RAG)Reinforcement Learning (RL)Regularization

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

Vision Transformer (ViT)Variational Autoencoder (VAE)Vector Embedding Vanishing / Exploding Gradient Validation Set

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

Yoga of AI Y-transform / YUV Yield (model yield / throughput)Y-axis / feature axis YAGNI (You Aren't Gonna Need It)

Z

Z-score Normalization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference Zero-centric / Zero-bias initialization Zygosity in augmentation

什么是通用逼近定理

通用逼近定理（Universal Approximation Theorem，简称 UA Theorem）是神经网络与函数逼近理论中的一个基本结果。它表明，具有足够隐藏层的前馈神经网络可以逼近任何连续函数。

该定理最早由 George Cybenko 在 1989 年提出，之后经过多次扩展。其核心思想是，尽管神经网络的结构可能复杂，但只要网络的隐藏层足够多，就能以任意精度逼近任何连续函数。

UA Theorem 的重要性在于它为深度学习的成功提供了理论依据，表明神经网络是一种强大的函数逼近工具。这一发现推动了神经网络的广泛应用，特别是在图像识别、自然语言处理等领域。

该定理通常应用于前馈神经网络，特别是单隐层的网络。通过适当的激活函数（如 sigmoid 或 ReLU），网络能够捕捉输入与输出之间的复杂关系。

随着深度学习技术的进步，UA Theorem 的应用将进一步扩展到更复杂的模型和算法中，特别是生成对抗网络（GANs）和强化学习框架。

尽管该定理的理论适用性广泛，但在实际应用中，网络的训练过程可能面临过拟合、收敛速度慢等问题。因此，理解 UA Theorem 对于从事机器学习和深度学习研究的人员至关重要，特别是在设计和优化神经网络时。