핵심 개념
Simplicial Map Neural Networks (SMNNs) address limitations through a new training procedure, enhancing efficiency and generalization.
초록
1. Introduction
AI methods have advanced, leading to complex self-regulated AI models.
Explainable Artificial Intelligence (XAI) aims to provide transparent explanations.
2. Background
Simplicial complexes consist of vertices and simplices.
Simplicial maps are used for classification tasks.
3. The unknown boundary and the function 𝜑𝑈
Introduces a method to compute a function approximating 𝜑 without a convex polytope.
4. Training SMNNs
Proposes learning 𝜑(0)𝑈 using gradient descent to minimize loss function.
통계
SMNNs는 적합한 조건 하에서 보안적 예제에 대해 강건성을 보여줍니다.
SMNNs는 높은 차원 데이터셋에서 적용 가능성에 제약이 있습니다.
인용구
"SMNNs present some bottlenecks for their possible application in high-dimensional datasets."
"SMNNs are explainable models since all decision steps to compute the output of SMNNs are understandable and transparent."