208 citations · 208 across the 2 of their papers we have counts for
6 papers
Unsupervised Domain Adaptation using Graph Transduction Games
Sebastiano Vascon, Sinem Aslan, Alessandro Torcinovich +3
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a rel…
Generative models for local network community detection
Twan van Laarhoven
Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much chea…
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
Jeroen Manders, Twan van Laarhoven, Elena Marchiori
We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of ta…
Domain Adaptation with Randomized Expectation Maximization
Twan van Laarhoven, Elena Marchiori
Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain. The majority of successful DA methods try to…
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders
Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo +5
Autism Spectrum Disorders are associated with atypical movements, of which stereotypical motor movements (SMMs) interfere with learning and social interaction. The automatic SMM de…
L2 Regularization versus Batch and Weight Normalization
Twan van Laarhoven
Batch Normalization is a commonly used trick to improve the training of deep neural networks. These neural networks use L2 regularization, also called weight decay, ostensibly to p…