I am Dennis Ulmer, PhD student for NLP at ITU.
My current research interests include uncertainty estimation methods with application to Natural Language Processing, learning for low-resource languages, compositionality, inductive biases and much more!
My background includes an undergraduate degree in Computational Linguistics from the University of Heidelberg and a master's degree in Artificial Intelligence from the University of Amsterdam, where I wrote
my thesis under Dieuwke Hupkes and Elia Bruni. I am currently a student under Natalie Schluter at the NLPnorth group @ IT University Copenhagen.
Questions or ideas for collaborations? Message me via the options on the left!
- Dennis Ulmer: "A Survey on Evidential Deep Learning for Single-Pass Uncertainty Estimation" (pre-print) [pdf]
- Dennis Ulmer, Giovanni Cinà: "Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection" (pre-print; accepted at UAI 2021) [pdf]
- Dennis Ulmer, Lotta Meijerink, Giovanni Cinà: "Trust issues: Uncertainty estimation does not enable reliable OOD detection on medical tabular data" (NeurIPS 2020 ML4H Workshop) [pdf]
- Joris Baan, Jana Leible, Mitja Nikolaus, David Rau, Dennis Ulmer, Tim Baumgärtner, Dieuwke Hupkes, Elia Bruni: "On the realization of compositionality in neural networks" (ACL 2019 BlackboxNLP Workshop) [pdf]
- Dennis Ulmer, Dieuwke Hupkes, Elia Bruni: "Assessing incrementality in sequence-to-sequence models" (ACL 2019 Repl4NLP Workshop) [pdf]
- Dennis Ulmer: "Recoding latent sentence representations - Dynamic gradient-based activation modification in RNNs" (Master Thesis) [pdf]
💻 Highlighted Coding Projects
Easy and Better Significance Testing for Deep Neural Networks.
A lightweight but powerful library for token indexing,
fully tested and compatible with PyTorch and Tensorflow.
OOD Detection For Electronic Health Records
Collection of a multitude of methods used for OOD detection on Electronic Health Records.