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In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
Like BERT (but unlike "bag of words" such as Word2Vec and GloVe), ELMo word embeddings are context-sensitive, producing different representations for words that share the same spelling. It was trained on a corpus of about 30 million sentences and 1 billion words. [4] Previously, bidirectional LSTM was used for contextualized word representation ...
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]
Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.
Removes the bias of subword tokenisation: where common subwords are overrepresented and rare or new words are underrepresented or split into less meaningful units. This can affect the model's understanding and generation capabilities, particularly for languages with rich morphology or tokens not well-represented in the training data.
Video has a temporal dimension that makes a TDNN an ideal solution to analysing motion patterns. An example of this analysis is a combination of vehicle detection and recognizing pedestrians. [ 15 ] When examining videos, subsequent images are fed into the TDNN as input where each image is the next frame in the video.
English: A diagram for a one-unit Long Short-Term Memory (LSTM). From bottom to top : input state, hidden state and cell state, output state. Gates are sigmoïds or hyperbolic tangents. Other operators : element-wise plus and multiplication. Weights are not displayed. Inspired from Understanding LSTM, Blog of C. Olah
The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins. [20] Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, [21] [22] and its connectionist temporal classification (CTC) training algorithm [23] in 2006. CTC was applied to end-to-end speech ...