Symmetrical SyncMap for Imbalanced General Chunking Problems
Heng Zhang Danilo Vasconcellos Vargas
Recently, SyncMap pioneered an approach to learn complex structures from
sequences as well as adapt to any changes in underlying structures. This is
achieved by using only nonlinear dynamical equations inspired by neuron group
behaviors, i.e., without loss functions. Here we propose Symmetrical SyncMap
that goes beyond the original work to show how to create dynamical equations
and attractor-repeller points which are stable over the long run, even dealing
with imbalanced continual general chunking problems (CGCPs). The main idea is
to apply equal updates from negative and positive feedback loops by symmetrical
activation. We then introduce the concept of memory window to allow for more
positive updates. Our algorithm surpasses or ties other unsupervised
state-of-the-art baselines in all 12 imbalanced CGCPs with various
difficulties, including dynamically changing ones. To verify its performance in
real-world scenarios, we conduct experiments on several well-studied structure
learning problems. The proposed method surpasses substantially other methods in
3 out of 4 scenarios, suggesting that symmetrical activation plays a critical
role in uncovering topological structures and even hierarchies encoded in
temporal data.