Implementing Neural Turing Machines

Collier, Martin; Beel, Joeran

doi:10.1007/978-3-030-01424-7_10

Cited by 47 publications

(41 citation statements)

References 11 publications

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“…Several tools can be applied in the implementations of NTM [83][84][85][86][87]; however, the evaluation parameters are independent of the tools. For the entire performed experiments, the RMSProp algorithm has been used for training with a momentum of 0.9.…”

Section: Tools Setup and Parametersmentioning

confidence: 99%

A Review on Neural Turing Machine (NTM)

Faradonbe

Safi-Esfahani

Karimian-kelishadrokhi

2020

SN COMPUT. SCI.

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One of the significant objectives of artificial intelligence is to design learning algorithms that are executed on general-purpose computational machines inspired by the human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. In the literature, a variety of approaches have been presented for the NTM; however, there is no existing comprehensive survey and taxonomy for NTM methods. This article presents an overview of taxonomies characterizing the critical concepts of the NTM through a comprehensive survey on the related research activities. This in-depth analysis of taxonomies can provide researchers, designers, and application developers with a clear guideline to compare NTM methods. The taxonomy of machine learning, neural networks, and the Turing machine is introduced. The NTM is also inspected in terms of concepts, structure, implemented tasks, and related works. The article further presents research discussions and future challenges in this area.

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Section: Tools Setup and Parametersmentioning

confidence: 99%

A Review on Neural Turing Machine (NTM)

Faradonbe

Safi-Esfahani

Karimian-kelishadrokhi

2020

SN COMPUT. SCI.

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“…Neural Turing Machine (NTM) explores the concept of evidently extending the context accumulator of RNN with an addressable external memory. They are an example of Memory Augmented Neural Networks, which decouple the computation from memory [13]. NTM have been shown to outperform LSTMs on sequence learning tasks demanding large memory for handling memorization of more extended contexts.…”

Section: Ntmmentioning

confidence: 99%

“…The interaction between the Controller and Memory matrix is carried out by reading and write heads. The memory matrix is initialized using schemes like Constant initialization or Truncated Normal distribution [13]. The NTM model can be trained by variants of stochastic gradients using backpropagation through time in case of an RNN based controller.…”

Section: Ntmmentioning

confidence: 99%

LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot

Patil

Mudaliar

Kamat

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language. With the advent of AI, chatbots have developed from being minor guideline-based models to progressively modern models. A striking highlight of the current chatbot frameworks is their capacity to maintain and support explicit highlights and settings of the discussions empowering them to have human interaction in real-time surroundings. The paper presents a detailed database concerning the models utilized to deal with the learning of long haul conditions in a chatbot. The paper proposes a novel crossbreed Long Short Term Memory based Ensemble model to retain the information in specific situations. The proposed model uses a characterized number of Long Short Term Memory Networks as a significant aspect of its working as one to create the aggregate forecast class for the information inquiry and conversation. We found that both of the ensemble methods LSTM and GRU work well in different dataset environments and the ensemble technique is an effective one in chatbot applications.

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“…This is the case for the Neural Turing Machine [3] for which a neural network is complemented by a flow control mechanism and memory capabilities. Although the enhanced network is differentiable end-to-end, it remains somehow difficult to parameterize and very sensitive to initialization values [4]. Complex neural networks have often proved difficult to train.…”

Section: Introductionmentioning

confidence: 99%

Learning Arithmetic Operations With A Multistep Deep Learning

Nollet

Lefort

Armetta

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Deep neural networks are difficult to train when applied to tasks that can be expressed as algorithmic procedures. In this article, we propose to study how the explicit guidance of a network through all steps of the algorithm, using external memory and active choice of inputs, can improve its learning capability. The idea is to take inspiration from a child's learning and running through a procedure via interaction with an external support such as a paper. We show that this mechanism applied to a simple multilayer perceptron can significantly improve its performance when learning either a multi-digit addition or multiplication, which are simple but yet challenging operations to learn.

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Implementing Neural Turing Machines

Cited by 47 publications

References 11 publications

A Review on Neural Turing Machine (NTM)

A Review on Neural Turing Machine (NTM)

LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot

Learning Arithmetic Operations With A Multistep Deep Learning

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