Benchmarking VAE latent features in downstream tasks for cancer related predictions

Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Variational Auto-Encoders (VAEs) are used to assist in this process. VAEs are a type of neural network seeking to encode data into a smaller dimension called latent space. These latent features are then used to do downstream task analysis such as...

A systematic approach to deal with highly imbalanced data when predicting flight cancellations and delays

As on-time performance is one of the main contributors to success in the world of commercial aviation, predictions on flight delays and cancellations can significantly improve operational efficiency and thus quality of service. Since flight delays and cancellations are occasional and infrequent events, operational on-time performance data is...

Predicting dairy cow parturition using real-time behavior data from accelerometers: A study in commercial setting