Learning Representations Using Complex-Valued Nets

Understand

Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals.

• It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform.
• We show how CVNNs can be used to learn complex representations of real valued time-series data.
• We present methods and results using a framework that can compose holomorphic and non-holomorphic functions in a multi-layer network using a theoretical result called the Wirtinger derivative.