Stochastic approximation is a foundation for many algorithms found in machine learning and optimization.

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions.

Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a hard problem due to the discontinuity of the spiking neuron model.

In machine teaching, a concept is represented by (and inferred from) a small number of labeled examples.

This allows the temporal dynamics to be well captured by a set of multi-resolution temporal clusters.