no code implementations • 26 Oct 2021 • Kostadin Cvejoski, Jannis Schuecker, Anne-Katrin Mahlein, Bogdan Georgiev
In this work we combine representation learning capabilities of neural network with agricultural knowledge from experts to model environmental heat and drought stresses.
no code implementations • 5 Oct 2021 • Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee, Georgios Arvanitidis
A recent line of work has established intriguing connections between the generalization/compression properties of a deep neural network (DNN) model and the so-called layer weights' stable ranks.
no code implementations • 9 Mar 2021 • Georgios Arvanitidis, Bogdan Georgiev, Bernhard Schölkopf
In this work we propose a surrogate conformal Riemannian metric in the latent space of a generative model that is simple, efficient and robust.
no code implementations • ICLR 2021 • Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee
We are largely motivated by the search for a soft measure that sheds further light on the decision boundary's geometry.
no code implementations • 23 Dec 2020 • Lukas Franken, Bogdan Georgiev, Sascha Mücke, Moritz Wolter, Raoul Heese, Christian Bauckhage, Nico Piatkowski
The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
no code implementations • 10 Dec 2020 • David Biesner, Kostadin Cvejoski, Bogdan Georgiev, Rafet Sifa, Erik Krupicka
Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates.
1 code implementation • 10 Dec 2020 • Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev, Christian Bauckhage, Cesar Ojeda
Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities.
no code implementations • 12 Nov 2020 • Victor Kolev, Bogdan Georgiev, Svetlin Penkov
Abstract reasoning and logic inference are difficult problems for neural networks, yet essential to their applicability in highly structured domains.
no code implementations • 9 Dec 2019 • Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev, Jannis Schuecker, Christian Bauckhage, Cesar Ojeda
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance.
no code implementations • 24 Jun 2019 • César Ojeda, Kostadin Cvejosky, Ramsés J. Sánchez, Jannis Schuecker, Bogdan Georgiev, Christian Bauckhage
Service system dynamics occur at the interplay between customer behaviour and a service provider's response.
1 code implementation • 29 Mar 2019 • Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.