Long Short-Term Memory

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  1. Long short-term memory recurrent neural networks (LSTM-RNNs) have been applied to various speech applications including acoustic modeling for statistical parametric speech synthesis.[1]
  2. To address this concern, this paper proposes a low-latency, streaming speech synthesis architecture using unidirectional LSTM-RNNs with a recurrent output layer.[1]
  3. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015.[2]
  4. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that is able to explain a portion of the returns of the LSTM.[2]
  5. The Long Short Term Memory Network (LSTM) is a type of RNN designed to solve both of these issues, experienced by RNNs during the training phase.[3]
  6. We start by presenting the RNN???s architecture and continue with the LSTM and its mathematical formulation.[3]
  7. He has also made contributions to the book Big Data and Machine Learning in Quantitative Investment, with focus on long short term memory networks.[3]
  8. The major component of all LSTM networks is a hidden layer (the LSTM layer) consisting of the memory cells.[4]
  9. The main architecture was based on one bidirectional LSTM layer (Fig. 1).[4]
  10. The LSTN network architecture based on one bidirectional LSTM layer.[4]
  11. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM).[5]
  12. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.[5]
  13. LSTM networks were introduced in the late 1990s for sequence prediction, which is considered one of the most complex DL tasks.[6]
  14. In the case of an LSTM, for each element in the sequence, there is a corresponding hidden state \(h_t\), which in principle can contain information from arbitrary points earlier in the sequence.[7]
  15. Pytorch’s LSTM expects all of its inputs to be 3D tensors.[7]
  16. # the first value returned by LSTM is all of the hidden states throughout # the sequence.[7]
  17. In this section, we will use an LSTM to get part of speech tags.[7]
  18. We’ll walk through the LSTM diagram step by step later.[8]
  19. I’ve described so far is a pretty normal LSTM.[8]
  20. A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al.[8]
  21. Take my free 7-day email course and discover 6 different LSTM architectures (with code).[9]
  22. A recent model, “Long Short-Term Memory” (LSTM), is not affected by this problem.[9]
  23. An LSTM layer consists of a set of recurrently connected blocks, known as memory blocks.[9]
  24. Long Short-Term Memory (LSTM) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs).[9]
  25. The purpose of this article is to explain LSTM and enable you to use it in real life problems.[10]
  26. The functioning of LSTM can be visualized by understanding the functioning of a news channel’s team covering a murder story.[10]
  27. The information that is no longer required for the LSTM to understand things or the information that is of less importance is removed via multiplication of a filter.[10]
  28. The first layer is an LSTM layer with 300 memory units and it returns sequences.[10]
  29. Therefore, these causes the need of Long Short Term Memory (LSTM) which is a special kind of RNN’s, capable of learning long-term dependencies.[11]
  30. The LSTM worked well — for a while.[12]
  31. It is still a recurrent network, so if the input sequence has 1000 characters, the LSTM cell is called 1000 times, a long gradient path.[12]
  32. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate.[13]
  33. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.[13]
  34. The advantage of an LSTM cell compared to a common recurrent unit is its cell memory unit.[13]
  35. By introducing Constant Error Carousel (CEC) units, LSTM deals with the vanishing gradient problem.[13]
  36. Compared with other machine learning techniques, LSTM turns out to be more powerful and accurate in revealing degradation patterns, enabled by its time-dependent structure in nature.[14]
  37. Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides.[15]
  38. However, in most articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training formulas are omitted altogether.[15]
  39. The goal of this tutorial is to explain the essential RNN and LSTM fundamentals in a single document.[15]
  40. We provide all equations pertaining to the LSTM system together with detailed descriptions of its constituent entities.[15]
  41. 2 BO-LSTM Model architecture, using a sentence from the Drug-Drug Interactions corpus as an example.[16]
  42. The sequence of vectors representing the ancestors of the terms is then fed into the LSTM layer.[16]
  43. After the LSTM layer, we use a max pool layer which is then fed into a dense layer with a sigmoid activation function.[16]
  44. 3 BO-LSTM unit, using a sequence of ChEBI ontology concepts as an example.[16]
  45. The input gate decides what information is relevant to update in the current cell state of the LSTM unit.[17]
  46. The output gate determines the present hidden state that will be passed to the next LSTM unit.[17]
  47. Forget gate : The first block represented in the LSTM architecture is the forget gate (f t ).[17]
  48. The LSTM variation is in terms of usage of gates to achieve better performance parameters over the basic LSTM.[17]
  49. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context.[18]
  50. Long Short Term Memory network (LSTM), developed by Hochreiter and Schmidhuber (1997), and promises to overcome this problem.[18]
  51. Similar to most RNNs, LSTM also uses derivative based methods to evolve itself.[18]
  52. LSTM uses several gates with different functions to control the neurons and store the information.[18]
  53. This topic explains how to work with sequence and time series data for classification and regression tasks using long short-term memory (LSTM) networks.[19]
  54. This diagram illustrates the architecture of a simple LSTM network for classification.[19]
  55. The network starts with a sequence input layer followed by an LSTM layer.[19]
  56. This diagram illustrates the architecture of a simple LSTM network for regression.[19]
  57. Here’s another diagram for good measure, comparing a simple recurrent network (left) to an LSTM cell (right).[20]
  58. Furthermore, while we’re on the topic of simple hacks, including a bias of 1 to the forget gate of every LSTM cell is also shown to improve performance.[20]
  59. As we can see from the image, the difference lies mainly in the LSTM’s ability to preserve long-term memory .[21]
  60. The secret sauce to the LSTM lies in its gating mechanism within each LSTM cell.[21]
  61. Before we jump into a project with a full dataset, let's just take a look at how the PyTorch LSTM layer really works in practice by visualizing the outputs.[21]
  62. Just like the other kinds of layers, we can instantiate an LSTM layer and provide it with the necessary arguments.[21]
  63. 2018) Long short-term memory and learning-to-learn in networks of spiking neurons.[22]
  64. Script identification in natural scene image and video frames using an attention based convolutional-LSTM network.[22]
  65. Automated groove identification and measurement using long short-term memory unit.[22]
  66. Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer.[22]
  67. The system has an associative memory based on complex-valued vectors and is closely related to Holographic Reduced Representations and Long Short-Term Memory networks.[23]
  68. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction.[24]
  69. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions.[24]
  70. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency.[24]
  71. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow.[24]
  72. Arguably LSTM’s design is inspired by logic gates of a computer.[25]
  73. Just like in GRUs, the data feeding into the LSTM gates are the input at the current time step and the hidden state of the previous time step, as illustrated in Fig.[25]
  74. Definition - What does Long Short-Term Memory (LSTM) mean?[26]


  1. 1.0 1.1 Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis – Google Research
  2. 2.0 2.1 EconStor: Deep learning with long short-term memory networks for financial market predictions
  3. 3.0 3.1 3.2 Long short term memory networks: Theory and applications
  4. 4.0 4.1 4.2 A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations
  5. 5.0 5.1 Long Short-Term Memory
  6. Deep Learning Long Short-Term Memory (LSTM) Networks: What You Should Remember
  7. 7.0 7.1 7.2 7.3 Sequence Models and Long-Short Term Memory Networks — PyTorch Tutorials 1.7.1 documentation
  8. 8.0 8.1 8.2 Understanding LSTM Networks -- colah's blog
  9. 9.0 9.1 9.2 9.3 A Gentle Introduction to Long Short-Term Memory Networks by the Experts
  10. 10.0 10.1 10.2 10.3 Long Short Term Memory
  11. Long Short Term Memory (LSTM) Networks in a nutshell
  12. 12.0 12.1 Long Short-Term Memory Networks Are Dying: What’s Replacing It?
  13. 13.0 13.1 13.2 13.3 Long short-term memory
  14. Long short-term memory for machine remaining life prediction
  15. 15.0 15.1 15.2 15.3 Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
  16. 16.0 16.1 16.2 16.3 BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies
  17. 17.0 17.1 17.2 17.3 Long Short Term Memory
  18. 18.0 18.1 18.2 18.3 Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding
  19. 19.0 19.1 19.2 19.3 Long Short-Term Memory Networks
  20. 20.0 20.1 A Beginner's Guide to LSTMs and Recurrent Neural Networks
  21. 21.0 21.1 21.2 21.3 Long Short-Term Memory: From Zero to Hero with PyTorch
  22. 22.0 22.1 22.2 22.3 A review on the long short-term memory model
  23. Associative Long Short-Term Memory
  24. 24.0 24.1 24.2 24.3 Using long short-term memory networks for river flow prediction
  25. 25.0 25.1 9.2. Long Short-Term Memory (LSTM) — Dive into Deep Learning 0.15.1 documentation
  26. What is Long Short-Term Memory (LSTM)?



Spacy 패턴 목록

  • [{'LOWER': 'long'}, {'LOWER': 'short'}, {'OP': '*'}, {'LOWER': 'term'}, {'LEMMA': 'memory'}]
  • [{'LEMMA': 'LSTM'}]