In this paper,we consider the learning problem where the projected data isfurther compressed byscalarquantization, which iscalled quantized compressivelearning. Generalization error bounds are derived for three models: nearest neighbor (NN) classifier, linear classifier and least squares regression.

We study the problem of combining neural networks with symbolic reasoning.

Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: " how