6 citations · 13 across the 5 of their papers we have counts for
5 papers
Adaptable Adapters
Nafise Sadat Moosavi, Quentin Delfosse, Kristian Kersting +1
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters provide a parameter-efficient alternative for the full finetuning in which we can…
Do Multilingual Language Models Capture Differing Moral Norms?
Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +3
Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may ca…
Neuro-Symbolic Verification of Deep Neural Networks
Xuan Xie, Kristian Kersting, Daniel Neider
Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handf…
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement
Xiaoting Shao, Karl Stelzner, Kristian Kersting
A key assumption of most statistical machine learning methods is that they have access to independent samples from the distribution of data they encounter at test time. As such, th…
Inferring Offensiveness In Images From Natural Language Supervision
Patrick Schramowski, Kristian Kersting
Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined…