The attention distribution is usually generated with content-based attention. The attending RNN generates a query describing what it wants to focus on. Each item is dot-producted with the query to produce a score, describing how well it matches the query. The scores are fed into a softmax to create the attention distribution.

To review, the Forget gate decides what is relevant to keep from prior steps. The input gate decides what information is relevant to add from the current step. The output gate determines what the next hidden state should be.Code DemoFor those of you who understand better through seeing the code, here is an example using python pseudo code.

These results nonetheless show that it could be feasible to develop recurrent neural network modelsable to infer input-output behaviours of real biological systems, enabling researchers to advance theirunderstanding of these systems even in the absence of detailed level of connectivity.

We show that GRU models with a hidden layersize of 4 units are able to accurately reproduce with high accuracy the system’sresponse to very different stimuli.

Recursive Neural Networks

Unity ML agents is a way for you to turn a video game into a Reinforcement Learning environment.

Teacher forcing is a training technique that isapplicable to RNNs that have connections from their output to their hidden states at thenext time step. (Left) At train time, we feed the correct outputy(t)drawn from the trainset as input toh(t+1). (Right) When the model is deployed, the true output is generallynot known. In this case, we approximate the correct outputy(t)with the model’s outputo(t), and feed the output back into the model.

the network typically learns to useh(t)as a kind of lossysummary of the task-relevant aspects of the past sequence of inputs up tot

Parameter sharingmakes it possible to extend and apply the model to examples of different forms(different lengths, here) and generalize across them. If we had separate parametersfor each value of the time index, we could not generalize to sequence lengths notseen during training, nor share statistical strength across different sequence lengthsand across different positions in time. Such sharing is particularly important whena specific piece of information can occur at multiple positions within the sequence.

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation

Using Convolutional Neural Networks to Classify Audio Signal in Noisy Sound Scenes

Seeing in the dark with recurrent convolutional neural networks