Maximilian Nickel
Maximilian Nickel is a Research Scientist at Facebook AI Research in New York. Before joining FAIR, he was a postdoctoral fellow at MIT where he was with the Laboratory for Computational and Statistical Learning and the Center for Brains, Minds and Machines. In 2013, he received his PhD with summa cum laude from the Ludwig Maximilian University Munich. From 2010 to 2013 he worked as a research assistant at Siemens Corporate Technology. His research centers around geometric methods for learning and reasoning with relational knowledge representations and their applications in artificial intelligence and network science.
Maximilian's Publications
July 16, 2018
RESEARCH
NLP
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find…
July 16, 2018
December 08, 2019
RESEARCH
NLP
Hyperbolic Graph Neural Networks
Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated by recent advances in geometric representation learning, we propose…
December 08, 2019
July 28, 2019
RESEARCH
COMPUTER VISION
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from…
Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel
July 28, 2019
