Sentence examples for hidden representation from inspiring English sources

Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input.

The network takes an input and constructs a hidden representation through hidden layer.

It comprises of an encoder that maps an input vector to a hidden representation and a decoder that maps this hidden representation back to a reconstructed input.

The output layer just tries to reconstruct the input so the hidden representation can be taken as a code to the original input.

Also, let the corresponding hidden representation of d (x,y) at hidden layer be (a^{(x,y)}in mathbb {R}^{K}) and the reconstructed output be ({tilde {d}}^{(x,y)}in mathbb {R}^{n}).

Formally, an autoencoder takes an input (x in [0,1]^d) and encodes it to the hidden representation (h in [0,1]^{d'}) through some mapping function s. begin{aligned} h=s(Wx+b) end{aligned}where s is any mapping function like sigmoid and W and b are the weight and bias from input to hidden layer.

They are a powerful tool in extracting hidden representations and producing a robust reconstruction for further predicting tasks.

One of the main novelties is the use of tissue types as an input feature, which stringently required the model's hidden representations be in a form that can be well-modulated by information specifying the different tissue types for splicing pattern prediction.

As described earlier, the main idea can be summarized as (i) finding hidden representations for the data points by mapping them into a subspace with the help of kernel and projection matrices and (ii) performing binary classification in this shared subspace using the task-specific classification parameters.

When we map the data points into a low dimensional latent subspace using the projection matrix A, we obtain their hidden representations in this shared subspace, i.e. H = A ⊤ K. Using a kernel-based formulation has three main implications: (i) We can apply our method to tasks with high dimensional representations such as genomic information and small sample size (i.e. large p, small n).

One approach with considerable potential is the hierarchical amalgamation of proteins into a profile or hidden Markov model representation, which would aggregate many proteins into a single statistical summary for comparative purposes instead of discarding all but one member of the cluster.