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  1. The classical example of a sequence model is the Hidden Markov Model for part-of-speech tagging. Another example is the conditional random field. A recurrent neural network is a network that maintains some kind of state. For example, its output could be used as part of the next input, so that information can propagate along as the network ...
  2. deeplearningmath.org

    8 Sequence Models. Sequence Models have been motivated by the analysis of sequential data such text sentences, time-series and other discrete sequences data. These models are especially designed to handle sequential information while Convolutional Neural Network are more adapted for process spatial information.
  3. towardsdatascience.com

    These examples show that there are different applications of sequence models. Sometimes both the input and output are sequences, in some either the input or the output is a sequence. Recurrent neural network (RNN) is a popular sequence model that has shown efficient performance for sequential data. Recurrent Neural Networks (RNNs)
  4. sciencedirect.com

    The Sequence Model is a basic task analysis, capturing users' actions while doing a task. Many teams create these models to guide detailed design. Sequences help define the scenarios of use that the product must support and identify lower-level usability issues. They are not "big picture" models that help open up innovative thinking, the ...
  5. analyticsvidhya.com

    DNA sequence analysis: Given a DNA sequence as input, we want our model to predict which part of the DNA belongs to which protein. Machine Translation: We input a sentence in one language, say French, and we want our model to convert it into another language, say English. Here, both the input and the output are sequences: Video activity ...
  6. developers.google.com

    Dec 19, 2024sequence model. #seq. A model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos. T. timestep. #seq. One "unrolled" cell within a recurrent neural network. For example, the following figure shows three timesteps (labeled with the subscripts t-1, t, and t+1):
  7. Sequence Model 2: Long Short-Term Memory Networks (LSTM) LSTM networks are a special kind of RNN-based sequence model that addresses the issues of vanishing and exploding gradients and are used in applications such as sentiment analysis. As we discussed above, LSTM utilizes the foundation of RNNs and hence is similar to it, but with the ...
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