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Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backprop-agation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WT A) competition to learn distinct patterns.

In many real-world scenarios, sequential data are time-series sampled from some underlying continuous-time process, so datasets consist of long, irregularly sampled sequences of varied lengths.

We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores.

Recent works in V ariational Inference have examined alternative criteria to the commonly used exclusive Kullback-Leibler divergence.

Does knowledge distillation really work?

The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming.

Nonetheless, modern machine learning is adaptive in its nature. Prior information about a model's performance on the test set inevitably influences