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Research collaboration between Qualcomm and UvA

Our Mission

UvA
Qualcomm

The mission of the QUVA-lab is to perform world-class research on deep vision. Such vision strives to automatically interpret with the aid of deep learning what happens where, when and why in images and video. Deep learning is a form of machine learning with neural networks, loosely inspired by how neurons process information in the brain. Research projects in the lab focus on learning to recognize objects in images from a single example, personalized event detection and summarization in video, and privacy preserving deep learning. The research is published in the best academic venues and secured in patents.

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Highlights

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QUVA Lustrum 2020

Oct 6, 2020

On Oct 6, we celebrate the lustrum of the QUVA deep vision lab, a collaboration between Qualcomm and U. Amsterdam. Please join us here. Keynote by Aapo Hyvarinen at 17:50 CEST and a panel discussion at 18:30.

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QUVA Colloqium

The Qualcomm-UvA Deep Vision Seminars is an Amsterdam meetup for people that are passionate about AI, machine learning, deep learning and computer vision. Our guest speakers come both from industry and academia, working for organizations such as DeepMind, Oxford University, Toyota Research Center and more. Subscribe to our Meetup page to be notified about upcoming talks, or watch past presentations here.

Work with the QUVA Lab

Interested in working with the QUVA Lab? We have open positions! See all here.

Master Thesis Intern | Object-centric Causal Representation Learning

Phillip Lippe, Sara Magliacane

In this master thesis project, we explore the intersection of object-centric and causal representation learning. Possible applications include the reinforcement learning domain.

Details and application

Master Thesis Intern | Representational Learning with Discrete Variables

Adeel Pervez, Efstratios Gavves

This project explores the use of learning discrete representations for computer vision applications with latent variable models.

Details and application

Master Thesis Intern | Discrete Latent Variable Models for Compression

Adeel Pervez, Efstratios Gavves

This project explores the use of latent variable models with discrete latent variables for compression.

Details and application

Contact

University of Amsterdam

  • Science Park 904, Room C3.250a
    1098XH Amsterdam
    The Netherlands

Secretary

  • Virgine Mes 
      v.m.j.mes at uva dot nl
      0205256409

  •  Twitter: @quvalab