What is the difference between residual and recurrent?

Recurrence was defined as the diagnosis entered in the clinical record more than 6 months after CCRT. Residual disease was defined as the diagnosis entered in the clinical record within 6 months after CCRT.

What are residual Nets?

A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers.

Is RNN and ResNet are same?

We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 and ImageNet dataset.

What is a residual lesion?

Based on our previous report,7 residual lesions are defined as those that appeared on the first postoperative study performed within 12 months of endarterectomy. Recurrent stenoses are defined as those that appeared subsequent to a normal duplex examination or more than 12 months after the initial examination.

What is residual neoplasm?

Definition. Remnant of a tumor or cancer after primary, potentially curative therapy.

Why residual blocks are used?

The residual blocks create an identity mapping to activations earlier in the network to thwart the performance degradation problem associated with deep neural architectures. The skip connections help to address the problem of vanishing and exploding gradients.

Why residual networks are used?

Residual networks solve degradation problem by shortcuts or skip connections, by short circuiting shallow layers to deep layers. We can stack Residual blocks more and more, without degradation in performance. This enables very deep networks to be built.

Is ResNet recurrent?

a novel recurrent unit with residual error. The residual is first introduced in the residual networks (ResNet) [18,19], which refreshes the top performance in ImageNet database [20]. Residual learning is proven to be effective to restrain vanishing gradient and exploding gradient in the very deep networks [18,19,21].

Is ResNet a Lstm?

First, ResNet extracts latent features of daily and weekly load data. Then, LSTM is applied to train the encoded feature vector with dynamics, and make prediction suitable for volatile load data.