Steering deep sequence model with prototypes

一种原型、序列的技术,应用在利用原型来操纵深度序列模型领域,能够解决无法为专家用户提供模型功能性等问题

Pending Publication Date: 2021-06-11
ROBERT BOSCH GMBH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although end-to-end training of deep neural networks can alleviate the need to manually manage data features, training alone cannot provide expert users with the functionality to manipulate the model

Method used

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Examples

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Embodiment Construction

[0014] Embodiments of the disclosure are described herein. It should be understood, however, that the disclosed embodiments are examples only and that other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present embodiment. As will be understood by persons of ordinary skill in the art, various features illustrated and described with reference to any one figure may be combined with features illustrated in one or more other figures to create features not explicitly shown or described. Example. Combinations of illustrated features provide representative embodiments of typical applications. However, various combinations and modifications of t...

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PUM

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Abstract

A deep sequence model with prototypes may be steered. A prototype overview is displayed, the prototype overview including a plurality of prototype sequences learned by a model through backpropagation, each of the prototype sequences including a series of events, wherein for each of the prototype sequences, statistical information is presented with respect to use of the prototype sequence by the model. Input is received adjusting one or more of the prototype sequences to fine-tune the model. The model is updated using the plurality of prototype sequences, as adjusted, to create an updated model. The model, as updated, is displayed in the prototype overview.

Description

technical field [0001] The present disclosure relates to utilizing prototypes to manipulate deep sequence models. Background technique [0002] Deep learning models are employed in sequence data analysis to aid in decision making. Deep sequence models such as recurrent neural networks (RNNs) can be used to predict patient states by modeling electronic health records (EHRs), analyzing the topic or sentiment of text, and understanding audio signals. Such techniques have achieved state-of-the-art results in various applications. [0003] Despite their excellent performance, RNNs are often viewed as "black boxes". This is due to its complex architecture as well as the huge size of the model weights. This lack of interpretability limits the adoption of RNNs in many critical decision-making scenarios, where understanding of the reasons behind predictions may be required. As an example, the EU's recent data protection regulations grant individuals a "right to interpretation" ov...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G06N3/04G06N3/08G16H50/70
CPCG16H50/30G16H50/70G06N3/084G06N3/045G06F3/04842G06F3/0482G06N3/044G06F3/04847G06N3/08
Inventor 徐盼盼任骝明遥程富瑞屈华民
Owner ROBERT BOSCH GMBH
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