Physical System for Non Time Sequence Data

• URL: http://arxiv.org/abs/2010.03206v1
• Date: Wed, 7 Oct 2020 06:27:15 GMT
• Title: Physical System for Non Time Sequence Data
• Authors: Xiongren Chen
• Abstract summary: We propose a novelty approach to connect machine learning to causal structure learning by jacobian matrix of neural network w.r.t. input variables.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novelty approach to connect machine learning to causal structure learning by jacobian matrix of neural network w.r.t. input variables. In this paper, we extend the jacobian-based approach to physical system which is the method human explore and reason the world and it is the highest level of causality. By functions fitting with Neural ODE, we can read out causal structure from functions. This method also enforces a important acylicity constraint on continuous adjacency matrix of graph nodes and significantly reduce the computational complexity of search space of graph.

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