Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment

Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment

Korkić, Armin (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Makrodimitris, S. (mentor)
Abdelaal, T.R.M. (mentor)
Charrout, M. (mentor)
Eltager, M.A.M.E. (mentor)
Reinders, M.J.T. (graduation committee)
Isufi, E. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-02

Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algorithms we hope to improve personalized treatment for cancer patients. These machine learning algorithms are trying to learn a (latent) representation of the input. The problem is that this representation is hard to interpret and to observe the actual connections between the input and the output. That is why these algorithms are considered to behave like a black-box. In this research, a benchmark is conducted to measure how sensitive these algorithms are to changes in their hyper-parameters. The focus of this experiment are different types of variational auto encoders. We will measure how sensitive they are to changes in their: latent space dimension, learning rate and type of optimizer. The models will be trained on a dataset that contains the RNA gene-expression of different types of cancer tissues. To conclude that the optimizer may play the most important role performance wise for VAE models. Using the optimizer Adam and RMSprop results overall in lower reconstruction loss and overall in a more consistent performance.

VAE
Benchmarking
Hyperparameters
Optimizer
Learning Rate
Latent Dimension
Cancer
Treatment
Auto-Encoders

http://resolver.tudelft.nl/uuid:008d6b64-ec9b-400b-8773-c7932ed3fac9

Student theses

bachelor thesis

© 2021 Armin Korkić