MSc Defences November/December
See the list of MSc defences at DIKU this fall/winter. The list will continuously be updated.
If the defences are announced as ‘online defence’, the student has to be alone in the room during the examination and assessment. Guests can participate online but the links for the defences are not public. If you want to be present during the defence, please contact email@example.com or the supervisor for a link.
IT & Cognition
Encoding Representation for machine translation
Algorithms that operate directly over meaning representations are becoming more popular in the field of Natural Language Processing. Most of current research has focused on developing new graph based algorithms for Natural Language Processing applications such as semantic role labeling, text classification and text generation. In this thesis I focus on expanding our understanding of the capabilities of this innovative family of algorithms.
To do so, in this thesis I apply Graph Convolutional Network (GCN) encoders to generate translation where the input is a semantic or a syntactic representation. While this has been done in the past, in
this project I aim to expand our knowledge of this model on two axes. First I use a challenge set approach to asses whether or not graph-based algorithms perform better than transformer machine translation systems in the context of Long Distance Dependencies. Then I dive into the application of graph convolutional network on low-resource machine translation.
I show that GCN encoders are able to boost the performance of machine translation systems trained over relative small number of examples. Then I present results that show that Long Distance Dependencies are also a challenge for GCN based machine translation systems, where only in some
cases GCN models outperform transformer based systems on the challenge sets that I utilize.
Time and place
26 November 09:30
Statistics & Mathematics
Appearance models from 3D shapes and optical images for insects embedded in amber
Image registration is the process of geometrically aling two or more images. In this thesis, we present an approach for building 2D to 3D image registration based on spline functions. The proposed algorithm minimizes the bending of paired images over bijective deformations. Thin Plate
Spline (TPS) interpolation is a commonly used 2D interpolation method.
It is physically meaningful in the sense that it assumes that there is a point
in the original shape, which corresponds to a new point under the new coordinates after the deformation.
Thin Plate Spline(TPS) interpolation is the numerical solution to this problem. Spline functions are used to map the 2D camera image to the flatten image, which can be obtain from libigl package in python. After that inverse the parametrization, map flatten image to the 3D model. This process completes the reconstruction from 2D to 3D. Place the camera in different positions to get multi-angle pictures. We can get the 3D model patches corresponding to the pictures. After maximizing the similarity of the images, they can be ”glued” together via transition and obtain a synthetic color appearance(RGB) of the 3D model.
Keywords: image registration, thin plate spline, parametrization, transition map, RGB
Francois Bernard Lauze
Date and Time
15 December 2021