19 citations · 34 across the 4 of their papers we have counts for
7 papers
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
Dennis Ulmer, Jes Frellsen, Christian Hardmeier
We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training mod…
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endang…
Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs
Dennis Ulmer
In Recurrent Neural Networks (RNNs), encoding information in a suboptimal or erroneous way can impact the quality of representations based on later elements in the sequence and sub…
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
Dennis Ulmer, Giovanni Cinà
A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples whi…
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
Dennis Ulmer, Lotta Meijerink, Giovanni Cinà
When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's predictio…
Assessing incrementality in sequence-to-sequence models
Dennis Ulmer, Dieuwke Hupkes, Elia Bruni
Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly d…