28 citations · 177 across the 28 of their papers we have counts for
7 papers · 1 filter
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Lu Yin, Vlado Menkovski, Yulong Pei +1
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a…
Calibrated Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of prod…
Process Discovery Using Graph Neural Networks
Dominique Sommers, Vlado Menkovski, Dirk Fahland
Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph…
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The re…
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
Yoeri Poels, Vlado Menkovski
The goal of a classification model is to assign the correct labels to data. In most cases, this data is not fully described by the given set of labels. Often a rich set of meaningf…
On Generalization of Graph Autoencoders with Adversarial Training
Tianjin Huang, Yulong Pei, Vlado Menkovski +1
Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature rep…
Hierarchical Semantic Segmentation using Psychometric Learning
Lu Yin, Vlado Menkovski, Shiwei Liu +1
Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tas…