Predicting bee trajectories using Recurrent Neural Networks
In this work, a Prediction model, using LSTMs and mixture density networks was trained to predict trajectories. The model is tested on simple models and then applied to bee trajectories. A simulation was implemented that can run different movement models. The simulation offers a visualization of the movement models. After the Prediction models were trained an analysis that made use of the hidden states of the model was done. Plotting T-SNE and UMAP projections revealed interesting clusters in the hidden states. Furthermore, a classification task was solved to see if the hidden states of the Prediction model are able to boost classification performance. The results revealed that if the Prediction model is able to predict realistic trajectories, classification performance can be improved for problems where few labels are available. All the easy movement models were to some extend successfully learned by the Prediction model. The Prediction model was not able to predict realistic bee trajectories.