1 citations · 1 across the 3 of their papers we have counts for
4 papers
Equivariant Representation Learning in the Presence of Stabilizers
Luis Armando Pérez Rey, Giovanni Luca Marchetti, Danica Kragic +2
We introduce Equivariant Isomorphic Networks (EquIN) -- a method for learning representations that are equivariant with respect to general group actions over data. Differently from…
A Metric for Linear Symmetry-Based Disentanglement
Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski +2
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation tha…
Quantifying and Learning Linear Symmetry-Based Disentanglement
Loek Tonnaer, Luis A. Pérez Rey, Vlado Menkovski +2
The definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD. Su…
Complex Vehicle Routing with Memory Augmented Neural Networks
Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski
Complex real-life routing challenges can be modeled as variations of well-known combinatorial optimization problems. These routing problems have long been studied and are difficult…