deep-significance
Easy and Better Significance Testing for Deep Neural Networks.
I am Dennis Ulmer, PhD student for NLP at ITU.
I am currently a student under Christian Hardmeier at the NLPnorth group @ IT University Copenhagen and Jes Frellsen @ Technical University of Denmark (DTU).
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.
Questions or ideas for collaborations? Message me via the options on the left!
🎉 News
- 29.04.22: Our paper "Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective" won an oustanding paper award at the Machine Learning Evaluation Standards workshop at ICLR 2022!
- 29.04.22: I received an oustanding reviewer award at the Machine Learning Evaluation Standards workshop at ICLR 2022!
🎓 Publications
2022
- Dennis Ulmer, Christian Hardmeier, Jes Frellsen: "deep-significance: Easy and Meaningful Signifcance Testing in the Age of Neural Networks" (Machine Learning Evaluation Standards at ICLR 2022) [pdf] [code]
- Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Christian Hardmeier, Barbara Plank: "Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective" (selected as outstanding paper at Machine Learning Evaluation Standards at ICLR 2022) [pdf] [repo]
2021
- 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" (UAI 2021) [pdf] [code]
2020
- 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] [code]
2019
- 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] [code]
📑 Blog
🏆 Awards
- Outstanding paper award for Experimental Standards for Deep Learning Research: A Natural Language Processing perspective at Machine Learning Evaluation Standards Workshop at ICLR 2022
- Outstanding reviewer at Machine Learning Evaluation Standards Workshop at ICLR 2022
💻 Highlighted Coding Projects
📇⚡ token2index
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.