7 citations · 14 across the 15 of their papers we have counts for
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cs.LG2022★ 2 cited
Interpretability with full complexity by constraining feature information
Kieran A. Murphy, Dani S. Bassett
Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the e…
cs.LG2022
Characterizing information loss in a chaotic double pendulum with the Information Bottleneck
Kieran A. Murphy, Dani S. Bassett
A hallmark of chaotic dynamics is the loss of information with time. Although information loss is often expressed through a connection to Lyapunov exponents -- valid in the limit o…
cs.LG2022★ 3 cited
The Distributed Information Bottleneck reveals the explanatory structure of complex systems
Kieran A. Murphy, Dani S. Bassett
The fruits of science are relationships made comprehensible, often by way of approximation. While deep learning is an extremely powerful way to find relationships in data, its use…