14 citations · 33 across the 22 of their papers we have counts for
4 papers · 1 filter
The future is different: Large pre-trained language models fail in prediction tasks
Kostadin Cvejoski, Ramsés J. Sánchez, César Ojeda
Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. Yet, it is known that their performance can drastically drop…
Hidden Schema Networks
Ramsés J. Sánchez, Lukas Conrads, Pascal Welke +2
Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly. In this work we introduce a novel neural langua…
Informed Pre-Training on Prior Knowledge
Laura von Rueden, Sebastian Houben, Kostadin Cvejoski +2
When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that ha…
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner +49
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications…