Icon--AD-black-48x48Icon--address-consumer-data-black-48x48Icon--appointment-black-48x48Icon--back-left-black-48x48Icon--calendar-black-48x48Icon--Checkbox-checkIcon--clock-black-48x48Icon--close-black-48x48Icon--compare-black-48x48Icon--confirmation-black-48x48Icon--dealer-details-black-48x48Icon--delete-black-48x48Icon--delivery-black-48x48Icon--down-black-48x48Icon--download-black-48x48Ic-OverlayAlertIcon--externallink-black-48x48Icon-Filledforward-right_adjustedIcon--grid-view-black-48x48Icon--info-i-black-48x48Icon--Less-minimize-black-48x48Icon-FilledIcon--List-Check-blackIcon--List-Cross-blackIcon--list-view-mobile-black-48x48Icon--list-view-black-48x48Icon--More-Maximize-black-48x48Icon--my-product-black-48x48Icon--newsletter-black-48x48Icon--payment-black-48x48Icon--print-black-48x48Icon--promotion-black-48x48Icon--registration-black-48x48Icon--Reset-black-48x48share-circle1Icon--share-black-48x48Icon--shopping-cart-black-48x48Icon--start-play-black-48x48Icon--store-locator-black-48x48Ic-OverlayAlertIcon--summary-black-48x48tumblrIcon-FilledvineIc-OverlayAlertwhishlist

Important Cookie Information

This website uses cookies for reasons of functionality, comfort, and statistics. You can change those settings at any time in the footer on "Privacy Settings".

Bosch Center for Artificial Intelligence

Our Research Field

Large-Scale AI and Deep Learning

What Motivates Us

Most advances in novel machine learning algorithms are developed in an academic setting and based on smaller datasets. As a result, these algorithms are not ready for production use on real-world datasets. Scaling so far has been restricted to a select few industrial settings. To efficiently deploy AI at scale, we need to develop distributed machine learning algorithms and pipelines, and work on techniques to efficiently explore multidimensional hyper-parameter spaces.

Our Approach

One way to scale training of models is through multi-task architectures: these architectures exploit the correlation between several tasks to improve performance over individual tasks. As key enabler for wider applicability, multi-task architectures need to be able to learn from noisy, unlabeled or alternatively labeled data sets. Further, we investigate combination of distributed computing paradigms with techniques for efficient hyper-parameter optimization. To scale data preprocessing, we focus on techniques combining multiple sources of data to provide ground truth labels.

Application

Techniques which are able to effectively explore multi-dimensional hyper-parameter spaces are a key enabler across domains. At Bosch, we focus on deployment of these techniques for semantic search capabilities on large-scale video databases. Our research aims to provide accurate frame-level and scene-level tags for video sequences, enabling efficient search of desired scenes.

Read more

  • Boch BCAI - ICLR

    Zhang et al.

    "Deep learning on symbolic representations for large-scale heterogeneous time-series event..."
    • Authors: Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan
    • Published in IEEE Xplore in 2017
  • Bosch BCAI - Publication - ESANN

    Dhar et al.

    "Universum Learning for Multiclass SVM"
    • Authors: Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah
    • Published in SVM in 2016
  • Bosch BCAI - ADDM

    Dhar et al.

    "ADMM based Scalable Machine Learning on Spark"
    • Authors: Sauptik Dhar, Congrui Yi Naveen Ramakrishnan, Mohak Shah
    • Published in ADMM in 2015
  • Bosch BCAI - Publication - ICLR

    Heit et al.

    "An architecture for the deployment of statistical models for the big data era"
    • Authors: Juergen Heit, Jiayi Liu, Mohak Shah
    • Published in Big Data in 2016