12 citations · 22 across the 4 of their papers we have counts for
5 papers
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss
Ahmed Abdelwahab, Niels Landwehr
Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between ins…
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
Silvia Makowski, Lena Jäger, Ahmed Abdelwahab +2
We study the problem of inferring readers' identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generat…
A Semiparametric Model for Bayesian Reader Identification
Ahmed Abdelwahab, Reinhold Kliegl, Niels Landwehr
We study the problem of identifying individuals based on their characteristic gaze patterns during reading of arbitrary text. The motivation for this problem is an unobtrusive biom…
Varying-coefficient models with isotropic Gaussian process priors
Matthias Bussas, Christoph Sawade, Tobias Scheffer +1
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables. In varying-coefficient models with…
Learning to Identify Regular Expressions that Describe Email Campaigns
Paul Prasse, Christoph Sawade, Niels Landwehr +1
This paper addresses the problem of inferring a regular expression from a given set of strings that resembles, as closely as possible, the regular expression that a human expert wo…