Hypernetworks, or hypernets for short, are neural networks that generate weights for another
neural network, known as the target network. They have emerged as a powerful deep …

A central problem in drug discovery is to identify the interactions between drug-like
compounds and protein targets. Over the past few decades, various quantitative structure …

Exploring the integration of if-then logic rules within neural network architectures presents
an intriguing area. This integration seamlessly transforms the rule learning task into neural …

Computer vision plays a crucial role in enabling connected autonomous vehicles (CAVs) to
observe and comprehend their surroundings. The computer vision tasks are typically based …

HyperNetworks have been established as an effective technique to achieve fast adaptation
of parameters for neural networks. Recently, HyperNetworks conditioned on descriptors of …

Most medical images are low in contrast because adequate details that may prove vital
decisions are not visible to the naked eye. Also, due to the low‐contrast nature of the image …

Amodal object completion is a complex task that involves predicting the invisible parts of an
object based on visible segments and background information. Learning shape priors is …

The dominant image-to-image translation methods are based on fully convolutional
networks, which extract and translate an image's features and then reconstruct the image …

HyperNetworks have been established as an effective technique to achieve fast adaptation
of parameters for neural networks. Recently, HyperNetworks conditioned on descriptors of …

Neural networks whose weights are the output of a predictor (HyperNetworks) achieve
excellent performance on many tasks. In ConvNets, kernel prediction layers are a popular …