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

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

B

Batch Normalization Boosting Backpropagation Bias BERT

C

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

D

Deterministic Model Data Augmentation Deep Learning Discriminative Model Deepfake

E

Explainable AI (XAI)Ensemble Learning Encoder Embedding Epoch

F

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

G

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

H

Hierarchical Model Hidden Layer Hyperparameter Hallucination Heuristic

I

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

J

Juxtaposition Joint Embedding Jittering JAX JSONL / JSON-lines

K

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

L

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

M

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

N

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

O

Objective Function Overfitting Online Learning Optimizer One-hot Encoding

P

Parameter Policy / Reinforcement Learning Policy Prompt Pretraining Pooling

Q

Quantization Query Queue / Buffer Quality Estimation Q-learning

R

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

S

Supervised Learning Sampling Sequence Modeling Self-Supervised Learning Softmax

T

Transfer Learning Tokenizer Tuning / Hyperparameter Tuning Transformer Training Data

U

Uncertainty Estimation Unsupervised Learning Underfitting Universal Approximation Theorem U-Net

V

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

W

Whitening / Whitening Transformation Weak Supervision Word Embedding Workflow Weight Decay

X

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

Y

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

Z

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

XLM이란 무엇인가

XLM, 즉 Stellar Lumens는 국제 송금과 자산 전송을 촉진하기 위해 설계된 암호 화폐 및 블록체인 네트워크입니다. Stellar Development Foundation(SDF)에 의해 개발된 XLM은 글로벌 금융 시스템에 더 효율적인 솔루션을 제공하는 것을 목표로 하고 있습니다.

Stellar 네트워크의 핵심 철학은 빈곤 지역의 금융 서비스 문제를 해결하는 것으로, 저비용 거래 서비스를 제공하여 은행 서비스가 없는 인구를 돕고자 합니다. 빠른 거래 시간과 낮은 거래 수수료 덕분에 XLM은 국제 송금, 소액 결제 및 기타 금융 서비스에서 큰 잠재력을 보여줍니다.

앞으로 XLM은 전통 금융 기관과의 파트너십, 지속적인 사용자 기반 확장, 탈중앙화 금융(DeFi) 분야의 응용 프로그램에서의 사용이 기대됩니다. XLM은 효율성과 저비용이라는 많은 장점이 있지만 여전히 규제 문제와 시장 변동성 위험에 직면해 있습니다.