1 citations · 3 across the 7 of their papers we have counts for
7 papers
Mitigating Label Noise through Data Ambiguation
Julian Lienen, Eyke Hüllermeier
Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind…
Detecting Novelties with Empty Classes
Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier +1
For open world applications, deep neural networks (DNNs) need to be aware of previously unseen data and adaptable to evolving environments. Furthermore, it is desirable to detect a…
Memorization-Dilation: Modeling Neural Collapse Under Label Noise
Duc Anh Nguyen, Ron Levie, Julian Lienen +2
The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase o…
Kronecker Decomposition for Knowledge Graph Embeddings
Caglar Demir, Julian Lienen, Axel-Cyrille Ngonga Ngomo
Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results…
Conformal Credal Self-Supervised Learning
Julian Lienen, Caglar Demir, Eyke Hüllermeier
In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding…
Credal Self-Supervised Learning
Julian Lienen, Eyke Hüllermeier
Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances base…
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model
Julian Lienen, Eyke Hüllermeier, Ralph Ewerth +1
In many real-world applications, the relative depth of objects in an image is crucial for scene understanding. Recent approaches mainly tackle the problem of depth prediction in mo…