Trainable and Explainable Simplicial Map Neural Networks: Overcoming Limitations and Enhancing Training
Core Concepts
Simplicial Map Neural Networks (SMNNs) address limitations through a new training procedure, enhancing efficiency and generalization.
Abstract
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 ππ
Introduces a method to compute a function approximating π without a convex polytope.
4. Training SMNNs
Proposes learning π(0)π using gradient descent to minimize loss function.
Trainable and Explainable Simplicial Map Neural Networks Stats
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Quotes
"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."
Deeper Inquiries
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μ΄ λ
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ν μ μκΈ° λλ¬Έμ μ΄ λͺ¨λΈμ μ€μ μ°μ
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λΆμΌμμ νμ μ μΈ AI μ루μ
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Trainable and Explainable Simplicial Map Neural Networks: Overcoming Limitations and Enhancing Training
Trainable and Explainable Simplicial Map Neural Networks
μ΄λ»κ² SMNNμ μλ‘μ΄ νλ ¨ μ μ°¨κ° μ΄μ μ μ½μ 극볡νλ λ° λμμ΄ λ κΉμ?
μ΄λ»κ² SMNNμ μ€λͺ
κ°λ₯μ±μ΄ AI λͺ¨λΈμ μ λ’°μ±μ ν₯μμν€λ λ° λμμ΄ λ κΉμ?
μ΄ λ
Όλ¬Έμ κ²°κ³Όκ° μ€μ μ°μ
μμ©μ μ΄λ»κ² μ μ©λ μ μμκΉμ?
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