Deep transfer learning for classification of time-delayed Gaussian networks

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 from 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 from 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 from different classes of Gaussian networks are used collectively to pre-train a deep neural network governed by a Lyapunov stability condition. The proposed framework is validated on time series data sampled from synthetic Gaussian networks and applied to a real world dataset for the classification of basketball games based on skill level. We observe an improvement in the range of [15-25]% in accuracy and a saving in the range of [25-600]% in computational cost on synthetic as well as realistic networks with time-delays when compared to existing state-of-the-art approaches. In addition, new insights into meaningful offensive formations in the Basketball games can be derived from the deep network.