The Effect of Different Initialization Methods on VAEs for Modeling Cancer using RNA Genome Expressions

Variational Auto-Encoders are a class of machine learning models that have been used in varying context, such as cancer research. Earlier research has shown that initialization plays a crucial part in training these models, since it can increase performance. Therefore, this paper studies the effect initialization methods on VAEs. This research...

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

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...