14 citations · 23 across the 3 of their papers we have counts for
4 papers
Illuminating new and known relations between knot invariants
Jessica Craven, Mark Hughes, Vishnu Jejjala +1
We automate the process of machine learning correlations between knot invariants. For nearly 200,000 distinct sets of input knot invariants together with an output invariant, we at…
(K)not machine learning
Jessica Craven, Mark Hughes, Vishnu Jejjala +1
We review recent efforts to machine learn relations between knot invariants. Because these knot invariants have meaning in physics, we explore aspects of Chern-Simons theory and hi…
Learning knot invariants across dimensions
Jessica Craven, Mark Hughes, Vishnu Jejjala +1
We use deep neural networks to machine learn correlations between knot invariants in various dimensions. The three-dimensional invariant of interest is the Jones polynomial ,…
Disentangling a Deep Learned Volume Formula
Jessica Craven, Vishnu Jejjala, Arjun Kar
We present a simple phenomenological formula which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity. The avera…