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Found 4 papers, 0 papers with code
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain.
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms
This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment.
Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning
Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner by the use of more accurate execution predictions with a smaller safety margin.
Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training
When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods.
