5 citations · 19 across the 7 of their papers we have counts for
7 papers
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…
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…
Combining expert knowledge and neural networks to model environmental stresses in agriculture
Kostadin Cvejoski, Jannis Schuecker, Anne-Katrin Mahlein +1
In this work we combine representation learning capabilities of neural network with agricultural knowledge from experts to model environmental heat and drought stresses. We first d…
Generative Deep Learning Techniques for Password Generation
David Biesner, Kostadin Cvejoski, Bogdan Georgiev +2
Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates. In…
Recurrent Point Review Models
Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev +2
Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal informa…
Recurrent Point Processes for Dynamic Review Models
Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev +3
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance. Here, we incorporate temp…