Learning to Decouple Complex Systems

• URL: http://arxiv.org/abs/2302.01581v1
• Date: Fri, 3 Feb 2023 07:24:58 GMT
• Title: Learning to Decouple Complex Systems
• Authors: Zihan Zhou and Tianshu Yu
• Abstract summary: We propose a sequential learning approach for handling irregularly sampled and cluttered sequential observations. We argue that the meta-system evolving within a simplex is governed by projected differential equations (ProjDEs)
• Score: 16.544684282277526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics in the continuous-time domain; therein, complicated interactions between sub-systems also evolve over time. This setting is fairly common in the real world but has been less considered. In this paper, we propose a sequential learning approach under this setting by decoupling a complex system for handling irregularly sampled and cluttered sequential observations. Such decoupling brings about not only subsystems describing the dynamics of each latent entity but also a meta-system capturing the interaction between entities over time. Specifically, we argue that the meta-system evolving within a simplex is governed by projected differential equations (ProjDEs). We further analyze and provide neural-friendly projection operators in the context of Bregman divergence. Experimental results on synthetic and real-world datasets show the advantages of our approach when facing complex and cluttered sequential data compared to the state-of-the-art.

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