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- W4288106291 abstract "In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative configuration files. The data type abstraction allows for easier code and sub-model reuse, and the standardized interfaces imposed by this abstraction allow for encapsulation and make the code easy to extend. Declarative model definition configuration files enable inexperienced users to obtain effective models and increase the productivity of expert users. Alongside these two innovations, Ludwig introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks. These innovations make it possible for engineers, scientists from other fields and, in general, a much broader audience to adopt deep learning models for their tasks, concretely helping in its democratization." @default.
- W4288106291 created "2022-07-28" @default.
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- W4288106291 date "2019-09-17" @default.
- W4288106291 modified "2023-09-30" @default.
- W4288106291 title "Ludwig: a type-based declarative deep learning toolbox" @default.
- W4288106291 doi "https://doi.org/10.48550/arxiv.1909.07930" @default.
- W4288106291 hasPublicationYear "2019" @default.
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