38 citations · 146 across the 18 of their papers we have counts for
9 papers · 1 filter
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using we…
Learning to Learn from Weak Supervision by Full Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model…
Fidelity-Weighted Learning
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws +2
Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted exp…
On Search Powered Navigation
Mostafa Dehghani, Glorianna Jagfeld, Hosein Azarbonyad +3
Query-based searching and browsing-based navigation are the two main components of exploratory search. Search lets users dig in deep by controlling their actions to focus on and fi…
Finding Talk About the Past in the Discourse of Non-Historians
Alex Olieman, Kaspar Beelen, Jaap Kamps
A heightened interest in the presence of the past has given rise to the new field of memory studies, but there is a lack of search and research tools to support studying how and wh…
Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities
Alex Olieman, Kaspar Beelen, Milan van Lange +2
Over the last decade we have made great progress in entity linking (EL) systems, but performance may vary depending on the context and, arguably, there are even principled limitati…
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in…
Neural Ranking Models with Weak Supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn +2
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for info…
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter +3
A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies…