4 citations · 4 across the 2 of their papers we have counts for
3 papers
TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Kacper Cybiński, Björn van Zwol, James Enouen +5
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is gui…
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Kacper Cybiński, James Enouen, Antoine Georges +1
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating…
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
Kacper Cybiński, Marcin Płodzień, Michał Tomza +3
Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of…