… A CRDSA UE transmits replicas of the same packet in random slots in a global … networks
with low duty cycle UEs. Instead, ACRDA, although still uses replica transmissions at random …

… But our vision is to make the communication networks qualitatively more efficient, without
necessarily running an endless race towards increasing resources, but rather inventing a more …

… networks secured by random … random K-out graph over a set of n nodes is constructed
as follows. Each node draws an edge towards K distinct nodes selected uniformly at random…

… images and brain networks as we adapt the CNN paradigm to brain network data. To … shape
and performs a specific operation on the brain network. A BrainNetCNN layer contains one …

… To move towards manifold forests, we observe that random and oblique forests both sample
atoms from a dictionary to create their projected feature values, which are compared with …

… More precisely, we use quantum Generative Adversarial Networks (qGANs) to facilitate …
variational quantum circuit, and a classical neural network, the qGAN can learn a representation …

… Automatic segmentation of the liver and its lesion is an important step towards deriving … fully
convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs). We …

… Among the tremendous efforts toward achieving human-brain-… Neuromorphic computing, the
most powerful artificial networks for … -connected edge computing and deep neural networks. …

… 11] which attempt to combine the strengths of neural networks and random forests by using
… be equivalent to neural networks with many zeroed weights [11]. Random forests and other …

… network can automatically learn to discriminate between different classes of data, for example,
in this case, preictal and interictal. Generally, a classification neural network … to a random …