806 citations · 1.1k across the 47 of their papers we have counts for
6 papers · 1 filter
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…
Quantum Circuit Evolution on NISQ Devices
Lukas Franken, Bogdan Georgiev, Sascha Mücke +4
Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empir…
Towards Map-Based Validation of Semantic Segmentation Masks
Laura von Rueden, Tim Wirtz, Fabian Hueger +2
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not…
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks
Rajkumar Ramamurthy, Rafet Sifa, Christian Bauckhage
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated envi…
Learning Syllogism with Euler Neural-Networks
Tiansi Dong, Chengjiang Li, Christian Bauckhage +3
Traditional neural networks represent everything as a vector, and are able to approximate a subset of logical reasoning to a certain degree. As basic logic relations are better rep…
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…