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Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
A numeric character reference refers to a character by its Universal Character Set/Unicode code point, and a character entity reference refers to a character by a predefined name. A numeric character reference uses the format &#nnnn; or &#xhhhh; where nnnn is the code point in decimal form, and hhhh is the code point in hexadecimal form.
The input is a sequence of observations, and the outputs are a sequence of labels, which can include blank outputs. The difficulty of training comes from there being many more observations than there are labels. For example, in speech audio there can be multiple time slices which correspond to a single phoneme.
Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [143] M. Defferrard et al. Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text Classification 2014
Time Aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference. [1]
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
By contrast, a character entity reference refers to a sequence of one or more characters by the name of an entity which has the desired characters as its replacement text. The entity must either be predefined (built into the markup language), or otherwise explicitly declared in a Document Type Definition (DTD) (see [a]). The format is the same ...
The input text sequence is first mapped by an embedding layer into a sequence of vectors. Then two parts are run in parallel over it. The forward part is a 2-layered LSTM with 4096 units and 512 dimension projections, and a residual connection from the first to second layer.