Glossary

0-9

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

A

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

B

Backpropagation Batch Normalization BERT Bias Boosting

C

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

D

Data Augmentation Deep Learning Deepfake Deterministic Model Discriminative Model

E

Embedding Encoder Ensemble Learning Epoch Explainable AI (XAI)

F

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

G

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

H

Hallucination Heuristic Hidden Layer Hierarchical Model Hyperparameter

I

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

J

JAX Jittering Joint Embedding JSONL / JSON-lines Juxtaposition

K

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

L

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

M

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

N

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

O

Objective Function One-hot Encoding Online Learning Optimizer Overfitting

P

Parameter Policy / Reinforcement Learning Policy Pooling Pretraining Prompt

Q

Q-learning Quality Estimation Quantization Query Queue / Buffer

R

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

S

Sampling Self-Supervised Learning Sequence Modeling Softmax Supervised Learning

T

Tokenizer Training Data Transfer Learning Transformer Tuning / Hyperparameter Tuning

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weak Supervision Weight Decay Whitening / Whitening Transformation Word Embedding Workflow

X

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

Y

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

Z

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

What is Deterministic Model

A deterministic model is a mathematical representation where the outcome is predetermined by the input conditions, devoid of randomness. Such models are widely used in various fields including science, engineering, and economics.

The functioning of deterministic models is based on clear mathematical equations or algorithms. By inputting specific initial conditions, the model consistently generates the same output. Unlike stochastic models, deterministic models do not factor in random variables, leading to consistent results under identical conditions.

Typical applications include meteorology, where weather forecasts utilize deterministic models to predict future weather patterns. For instance, numerical weather prediction models employ atmospheric physics equations to simulate weather changes. Additionally, structural analysis in engineering often relies on deterministic models to assess building stability.

One major advantage of deterministic models is their high accuracy and repeatability, making them suitable for applications that require reliability. However, the limitation lies in their inability to account for uncertainties in complex systems, potentially leading to discrepancies in real-world predictions.

Looking forward, as computational capabilities and data science evolve, deterministic models are expected to integrate with other model types, such as stochastic and machine learning models, creating more comprehensive predictive systems. This combination will facilitate more accurate solutions when addressing complex real-world issues.

When utilizing deterministic models, it is crucial to be aware of the precision of model parameters and their applicable ranges. Furthermore, understanding the model’s limitations is essential to avoid over-reliance on its predictions during decision-making.

What is Deterministic Model - Glossary