Temporal convolutional networks tutorial

1. What are temporal convolutional networks?
2. How does TCN works?
3. What is TCN deep learning?
4. What is temporal neural network?

What are temporal convolutional networks?

Temporal convolutional network (TCN) is a recently proposed convolutional neural network, which combines the 1-dimensional fully convolutional network (1D FCN) and causal convolutions (Bai et al. 2018). The 1D FCN keeps the network producing an output of the same length as the input.

How does TCN works?

Temporal Convolutional Network

The TCN is designed from two basic principles: The convolutions are causal, meaning that there is no information leakage from future to past. The architecture can take a sequence of any length and map it to an output sequence of the same length just as with an RNN.

What is TCN deep learning?

Temporal convolutional network (TCN) is a framework which employs casual convolutions and dilations so that it is adaptive for sequential data with its temporality and large receptive fields.

What is temporal neural network?

Temporal neural networks (TNNs) are SNNs that communicate and process information encoded as relative spike times (in contrast to spike rates). A TNN architecture is proposed, and, as a proof-of-concept, TNN operation is demonstrated within the larger context of online supervised classification.