Detecting Phase Transitions Through Persistent Homology

Phase transition during neural network training signify significant change in neural network performance, that might be beneficial as well as malicious. Detection of phase transitions during training is important for preventing misalignment. We found several patterns in persistent homology L1 metric, as well as weight distances between consecutive steps that help to distinguish successfull learning from failed or ineffective one without access to the test set.