Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

This work explores hypernetworks: an approach of using a one network, also known as a
hypernetwork, to generate the weights for another network. Hypernetworks provide an …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic
information about good policies can be extracted from offline data which lack explicit …

The idea of using evolutionary computation to train artificial neural networks, or
neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 …

It has been observed that residual networks can be viewed as the explicit Euler
discretization of an Ordinary Differential Equation (ODE). This observation motivated the …

Hypernetworks are meta neural networks that generate weights for a main neural network in
an end-to-end differentiable manner. Despite extensive applications ranging from multi-task …

Dealing with high-dimensional input spaces, like visual input, is a challenging task for
reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to …

Many different deep networks have been used to approximate, accelerate or improve
traditional image operators. Among these traditional operators, many contain parameters …