  Parameterized mathematical models play a central role in understanding and
design of complex information systems. However, they often cannot take into
account the intricate interactions innate to such systems. On the contrary,
purely data-driven approaches do not need explicit mathematical models for data
generation and have a wider applicability at the cost of interpretability. In
this paper, we consider the design of a one-bit compressive autoencoder, and
propose a novel hybrid model-based and data-driven methodology that allows us
to not only design the sensing matrix for one-bit data acquisition, but also
allows for learning the latent-parameters of an iterative optimization
algorithm specifically designed for the problem of one-bit sparse signal
recovery. Our results demonstrate a significant improvement compared to
state-of-the-art model-based algorithms.
