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  • LSTM
  • We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. (springer.com)
  • The 'h' refers to the hidden state and the 'c' refers to the cell state used by an LSTM network. (apple.com)
  • This paper presents a model based on Recurrent Neural Network architecture, in particular LSTM, for modeling the behavior of Large Hadron Collider superconducting magnets. (springer.com)
  • You may provide the LSTM unit with a single input or a sequence of inputs. (apple.com)
  • Recurrent neural networks applied to sequence understanding (RNN, LSTM and GRUs), and other novel schemes such as Deep Reinforcement Learning or Generative Adversarial Networks, which can alleviate the need for large scale labelled data. (uoc.edu)
  • generates
  • The next word is predicted, and the network also generates a representation of its state for the input 'O', referred to as f ('O'). The next input word 'Romeo' is combined with the previous state, f ('O'), to create the next input. (apple.com)
  • 2018
  • Yue, Feng 2018-02-21 00:00:00 Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. (deepdyve.com)
  • protein
  • The protein sequence sets used in this study are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp (Bjorklund et al. (edu.in)
  • 2005). The dataset contains 578 experimental allergens and 700 non allergens protein sequences derived from food. (edu.in)
  • Sparse
  • Contrary to the dominant methodology, which relies on hand-crafted features that are manually engineered to be optimal for a specific task, our neural model automatically learns a sparse shift-invariant representation of the local 2D+t salient information, without any use of prior knowledge. (videolectures.net)
  • Recognition
  • Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. (coursera.org)
  • Be able to apply sequence models to audio applications, including speech recognition and music synthesis. (coursera.org)
  • matrices
  • We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. (deepdyve.com)
  • models
  • of models that can be used for sequences. (coursera.org)
  • Be able to apply sequence models to natural language problems, including text synthesis. (coursera.org)
  • deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. (coursera.org)
  • reads
  • Nanopore sequencing is a rapidly maturing technology delivering long reads in real time on a portable instrument at low cost. (springer.com)
  • Long reads are extremely valuable because they provide information on how distal sequences are spatially related. (springer.com)
  • layer
  • Our results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes. (springer.com)
  • that has a recurrent neural network layer, with its state input and output features listed. (apple.com)
  • What we're going to do is take the first word and feed it into a neural network layer. (coursera.org)
  • So that's a hidden layer of the first neural network. (coursera.org)
  • A recurrent neural network layer for inference on Metal Performance Shaders images. (apple.com)
  • A description of a simple recurrent block or layer. (apple.com)
  • In a traditional recurrent neural network, during the gradient back-propagation phase, the gradient signal can end up being multiplied a large number of times (as many as the number of timesteps) by the weight matrix associated with the connections between the neurons of the recurrent hidden layer. (danmackinlay.name)
  • sentence
  • This network was trained to generate the rest of a sentence from the play, given two prompt words from a sentence. (apple.com)
  • Approach
  • Rather than the commonly used sequencing-by-synthesis approach, nanopores directly sense DNA or RNA bases by means of pores that are embedded in a membrane separating two compartments. (springer.com)
  • The core of our long-term approach remains focused on creating a network of neuromorphic regions that provide the mechanisms needed to meet these requirements. (umd.edu)