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

可解释性是什么

可解释性是指人类理解模型或算法所做决策原因的程度。在人工智能和机器学习领域，随着模型复杂性的增加，可解释性变得越来越重要。

随着模型变得更加复杂，决策过程可能类似于“黑箱”，使得用户难以理解结论是如何得出的。这种复杂性引发了对可解释性研究的兴趣，特别是在医疗和金融等高风险领域，模型决策的透明度直接影响伦理和法律责任。

实现可解释性的技术包括特征重要性分析、可视化工具以及局部可解释模型（如 LIME 和 SHAP）。这些工具帮助用户理解模型决策的基础。

随着法规和标准的日益重视，尤其是欧盟的AI法规，可解释性将成为模型设计和开发的关键方面。

可解释性的优点包括增强信任和透明度，但过分追求可解释性可能会限制模型的复杂性和性能。开发者必须在模型的准确性与可解释性之间寻求平衡，以确保最终用户获得有用信息。