20 citations · 44 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Plasma Confinement Mode Classification Using a Sequence-to-Sequence Neural Network With Attention
Francisco Matos, Vlado Menkovski, Alessandro Pau +2
In a typical fusion experiment, the plasma can have several possible confinement modes. At the TCV tokamak, aside from the Low (L) and High (H) confinement modes, an additional mod…
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Deep learning achieves state-of-the-art performance in many tasks but exposes to the underlying vulnerability against adversarial examples. Across existing defense techniques, adve…
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…
Explaining Predictions by Approximating the Local Decision Boundary
Georgios Vlassopoulos, Tim van Erven, Henry Brighton +1
Constructing accurate model-agnostic explanations for opaque machine learning models remains a challenging task. Classification models for high-dimensional data, like images, are o…
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the…