In this paper, we propose deep transfer learning for classification of Gaussian networks with

time-delayed regulations. To ensure robust signaling, most real world problems from

related domains have inherent alternate pathways that can be learned incrementally a

stable form of the baseline. In this paper, we leverage on this characteristic to address the

challenges of complexity and scalability. The key idea is to learn high dimensional network

motifs low dimensional forms through a process of transfer learning. In contrast to

previous work, we facilitate positive transfer by introducing a triangular inequality

constraint, which provides a measure for the feasibility of mapping between different

motif manifolds. Network motifs different classes of Gaussian networks are used

collectively to pre-train a deep neural network governed by a Lyapunov stability condition.

The propose-In this paper, we propose deep transfer learning for classification of Gaussian networks with

time-delayed regulations. To ensure robust signaling, most real world problems from

related domains have inherent alternate pathways that can be learned incrementally　a

stable form of the baseline. In this paper, we leverage on this characteristic to address the

challenges of complexity and scalability. The key idea is to learn high dimensional network

motifs　low dimensional forms through a process of transfer learning. In contrast to

previous work, we facilitate positive transfer by introducing a triangular inequality

constraint, which provides a measure for the feasibility of mapping between different

motif manifolds. Network motifs　different classes of Gaussian networks are used

collectively to pre-train a deep neural network governed by a Lyapunov stability condition.

The propose