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Most existing work learn the weights by introducing a hyperparameter, which is undesired in practice.
Levenberg Marquart algorithm is used to learn the weights of FLANN.
Normally a backpropagation algorithm is applied to learn the weights in the network.
There is no method to learn the weights using the maximum likelihood (ML) approach.
We propose a mixed negative instance sampling strategy to learn the weights of different joint feature representations.
Using the known disparity patches, we can train the sparse autoencoder to learn the weights (W,U,r,s).
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A direction that remains to be explored is using attention mechanism to learn the weight of word automatically.
SVM, or any other standard machine learning method, cannot learn the weight of a singleton triplet from the training set.
Compared with MKL approaches that learn one weight for one kernel matrix representing one modality, our method will learn the weight for each feature to capture the local feature importance.
Learn the Weight Watchers program, and adapt it into your everyday life.
On the other hand, in unsupervised learning the weights are modified in response to network inputs only.
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