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Neural Power Units

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00347207" target="_blank" >RIV/68407700:21230/20:00347207 - isvavai.cz</a>

  • Result on the web

    <a href="https://proceedings.neurips.cc/paper/2020/hash/48e59000d7dfcf6c1d96ce4a603ed738-Abstract.html" target="_blank" >https://proceedings.neurips.cc/paper/2020/hash/48e59000d7dfcf6c1d96ce4a603ed738-Abstract.html</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Neural Power Units

  • Original language description

    Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Units aim to overcome this difficulty, but current arithmetic units are either limited to operate on positive numbers or can only represent a subset of arithmetic operations. We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer. The NPU thus fixes the shortcomings of existing arithmetic units and extends their expressivity. We achieve this by using complex arithmetic without requiring a conversion of the network to complex numbers. A simplification of the unit to the RealNPU yields a highly transparent model. We show that the NPUs outperform their competitors in terms of accuracy and sparsity on artificial arithmetic datasets, and that the RealNPU can discover the governing equations of a dynamical system only from data.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

  • ISBN

  • ISSN

    1049-5258

  • e-ISSN

    1049-5258

  • Number of pages

    11

  • Pages from-to

  • Publisher name

    Neural Information Processing Society

  • Place of publication

    Montreal

  • Event location

    Vancouver

  • Event date

    Dec 6, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article