Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

Human visual perception carves a scene at its physical joints, decomposing the world into
objects, which are selectively attended, tracked and predicted as we engage our …

Complex-valued neural networks have many advantages over their real-valued
counterparts. Conventional digital electronic computing platforms are incapable of executing …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Human perception is structured around objects which form the basis for our higher-level
cognition and impressive systematic generalization abilities. Yet most work on …

At present, the vast majority of building blocks, techniques, and architectures for deep
learning are based on real-valued operations and representations. However, recent work on …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

Many real world tasks such as reasoning and physical interaction require identification and
manipulation of conceptual entities. A first step towards solving these tasks is the automated …

Photonic neural networks perform brain-inspired computations using photons instead of
electrons to achieve substantially improved computing performance. However, existing …

The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …