LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Back to previous search Search history
Advanced search

Your last searches

  1. AU="Lutscher, Daniel"
  2. AU="Ceretta Moreira, Eduardo"
  3. AU="Nalbant, Elif"

Search results

Result 1 - 2 of total 2

Search options

  1. Article ; Online: CO

    Brockmüller, Eike / Wellmann, Felix / Lutscher, Daniel / Kimmelma, Ossi / Lowder, Tyson / Novotny, Steffen / Lachmayer, Roland / Neumann, Jörg / Kracht, Dietmar

    Optics express

    2022  Volume 30, Issue 15, Page(s) 25946–25957

    Abstract: We report on the development of a side-fused signal-pump combiner with an integrated feed-through 34/250-µm chirally coupled core fiber. The manufacturing process involves a novel rotationally symmetrical cladding restructuring using a ... ...

    Abstract We report on the development of a side-fused signal-pump combiner with an integrated feed-through 34/250-µm chirally coupled core fiber. The manufacturing process involves a novel rotationally symmetrical cladding restructuring using a CO
    Language English
    Publishing date 2022-10-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.455606
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Book ; Online: Mixing Consistent Deep Clustering

    Lutscher, Daniel / Hassouni, Ali el / Stol, Maarten / Hoogendoorn, Mark

    2020  

    Abstract: Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature regarding representation learning, studies suggest that one key ... ...

    Abstract Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature regarding representation learning, studies suggest that one key characteristic of good latent representations is the ability to produce semantically mixed outputs when decoding linear interpolations of two latent representations. We propose the Mixing Consistent Deep Clustering method which encourages interpolations to appear realistic while adding the constraint that interpolations of two data points must look like one of the two inputs. By applying this training method to various clustering (non-)specific autoencoder models we found that using the proposed training method systematically changed the structure of learned representations of a model and it improved clustering performance for the tested ACAI, IDEC, and VAE models on the MNIST, SVHN, and CIFAR-10 datasets. These outcomes have practical implications for numerous real-world clustering tasks, as it shows that the proposed method can be added to existing autoencoders to further improve clustering performance.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-11-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top