In the current era of high energy particle collider experiments, we are faced with an overwhelming amount of data and the limiting uncertainty in new physics searches can often come from theory and not experiment. In our efforts to develop new approaches to extract complex signals from large backgrounds, BDTs, neural networks and other machine learning techniques are becoming increasingly significant. These tools allow us to find patterns in data that would be impossible to identify with a simple cut-and-count approach. In this work we show how unsupervised learning approaches based on deep autoencoders can be directly trained on data and used for model-independent searches for new physics. Beyond autoencoder we will discuss progress in applying and understanding the four-vector based Lorentz-layer approach to new challenges.