Feature decoupling is a technique used in machine learning and feature engineering to improve the performance and interpretability of models by separating correlated or redundant features into independent components. The main goal of feature decoupling is to simplify the representation of data while retaining the most relevant information for the task at hand.
Dual learning is a paradigm in machine learning that leverages the duality between two related tasks to improve learning efficiency and performance. The basic idea of dual learning is to train two models simultaneously, each focusing on a different aspect of the problem, while using feedback from one model to improve the learning of the other.
Contrastive learning is a machine learning technique used for representation learning, where the goal is to learn useful representations of data points by contrasting them with each other. The basic idea is to encourage similar data points to be closer to each other in the learned representation space while pushing dissimilar data points further apart.
Explainable Artificial Intelligence (XAI) refers to the set of techniques and methodologies aimed at making the outputs and decisions of AI systems understandable to humans. The goal of XAI is to increase transparency, trust, and accountability in AI systems by providing insights into how they arrive at their conclusions.
FreeSurfer is a software suite used for the analysis and processing of neuroimaging data, particularly focusing on structural brain MRI scans. It’s an open-source software package developed by the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School.
當評估機器學習模型時，常見的指標可以用簡單的方式解釋：
這些指標可以幫助了解模型在不同問題類型中的表現，從而選擇最適合的模型以及改進模型效能。
Federated AI Technology Enabler (FATE) is an open-source computing framework designed for federated learning scenarios. It aims to support privacy protection and secure computation in federated learning environments.
Federated learning is a decentralized machine learning approach that allows training models across multiple devices or data sources without centralizing the data in one location.
FATE provides a means for participants to conduct model training and inference while preserving data privacy.
“Fairness and Machine Learning” 是指在機器學習和人工智慧領域中，關注如何確保演算法和模型的公平性、公正性和無偏見性。在這個領域，人們致力於解決機器學習模型可能存在的偏見或歧視問題，確保演算法在處理資料和做出決策時不偏袒或歧視特定群體。
這個領域涉及到倫理、社會公正和演算法公平性等多個方面。它強調了在使用機器學習技術時應該考慮到對不同群體的平等對待，並努力消除因種族、性別、年齡、性取向等因素而引起的偏見。研究人員和從業者嘗試開發演算法和技術，以確保模型的公正性，並提出了各種衡量和評估公平性的方法和指標，以便在設計和評估機器學習系統時能夠更好地考慮到公平性問題。
安全屋（Trusted Execution Environment，TEE）是一種硬體和軟體的結合，提供了一個安全的執行環境，用於保護應用程式和敏感數據免受外部攻擊或未授權訪問。
TEE通常位於計算裝置（如智能手機、物聯網設備或服務器）中，它提供了一個被信任的區域，可以在其中執行被稱為「安全執行環境」的受保護區域。這個受保護的環境是隔離的，並且有自己的受保護的記憶體和程序運行空間。
同態加密（Homomorphic Encryption）是一種特殊類型的加密技術，允許對加密資料進行計算，而無需先解密它們。這種加密方法允許對加密資料進行某些特定操作（如加法、乘法等），並且在對加密資料進行計算後，結果仍然是加密的。最後，只有當持有解密金鑰的合法用戶對計算結果進行解密時，才能獲得正確的最終結果。
同態加密在隱私保護和安全計算中具有重要作用。它允許資料持有者將資料加密後外包給其他方進行計算，而不會洩露資料內容。例如，可以在加密的資料上執行各種計算，如在雲計算環境中對加密資料進行處理，而無需直接訪問解密後的原始資料。這樣做可以確保資料隱私，在資料處理和共用過程中提供更高的安全性。
雖然同態加密技術在理論上非常強大，但目前仍存在一些挑戰，例如運算效率較低、複雜度高等問題。然而，隨著技術的進步和研究的不斷深入，同態加密正逐漸成為隱私保護和安全計算領域的一個有潛力的解決方案。