Sökning: "Learning Objects"

Visar resultat 16 - 20 av 237 avhandlingar innehållade orden Learning Objects.

1. 16. Learning visual perception for autonomous systems

   Författare :Gustav Häger; Michael Felsberg; Per-Erik Forssén; Fahad Shahbaz Khan; Roman Pflugfelder; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; computer vision; visual object tracking; tracking; machine learning; deep learning;

   Sammanfattning : In the last decade, developments in hardware, sensors and software have made it possible to create increasingly autonomous systems. These systems can be as simple as limited driver assistance software lane-following in cars, or limited collision warning systems for otherwise manually piloted drones. LÄS MER

3. 17. Learning science by digital technology : Students’ understanding of computer animated learning material

   Författare :Göran Karlsson; Berner Lindström; Göteborgs universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES;

   Sammanfattning : Digital learning material is associated with grand expectations among educational policy makers. Several attempts to introduce this new technology with the purpose of enhancing learning have been made in recent years. The schooling system has, however, been rather hesitant and not so ready to adopt this kind of teaching aid. LÄS MER

4. 18. Data-Efficient Representation Learning for Grasping and Manipulation

   Författare :Ahmet Ercan Tekden; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Learning from Demonstration; Generative Modeling; Neural Fields; Robot Learning; Data-efficient Representation Learning; Grasping; Robot Manipulation;

   Sammanfattning : General-purpose robotics require adaptability to environmental variations and, therefore, need effective representations for programming them. A common way to acquire such representations is through machine learning. Machine learning has shown great potential in computer vision, natural language processing, reinforcement learning, and robotics. LÄS MER

5. 19. Development and application of rule- and learning-based approaches within the scope of neuroimaging : Tensor voting, tractography and machine learning

   Författare :Daniel Jörgens; Rodrigo Moreno; Örjan Smedby; Chunliang Wang; Jesper Andersson; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; tensor voting; tractography; deep learning; tractogram filtering; diffusion magnetic resonance imaging; tensorröstning; traktografi; djupinlärning; traktogramfiltrering; diffusions-MRT; Tillämpad medicinsk teknik; Applied Medical Technology;

   Sammanfattning : The opportunity to non-invasively probe the structure and function of different parts of the human body makes medical imaging an indispensable tool in clinical diagnostics and related fields of research. Especially neuroscientists rely on modalities like structural or functional Magnetic Resonance Imaging, Computed Tomography or Positron Emission Tomography to study the human brain in vivo. LÄS MER

7. 20. Structured Representations for Explainable Deep Learning

   Författare :Federico Baldassarre; Hossein Azizpour; Josephine Sullivan; Kevin Smith; Hamed Pirsiavash; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Datalogi; Computer Science;

   Sammanfattning : Deep learning has revolutionized scientific research and is being used to take decisions in increasingly complex scenarios. With growing power comes a growing demand for transparency and interpretability. The field of Explainable AI aims to provide explanations for the predictions of AI systems. LÄS MER