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.

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

Sec. 2 provides more experimental details on Few-shot Learning for our GraphAdapter. Sec. 3 describes more details about datasets and implementation. Sec. 4 visualizes the textual graph nodes used for classification before and after utilizing our Sec. Notably, the TaskRes* exploits the enhanced base classifier. We present the numerical results of "Figure 3 in the main text" as Table 2.