no code implementations • 20 Jan 2023 • Girmaw Abebe Tadesse, Jannis Born, Celia Cintas, William Ogallo, Dmitry Zubarev, Matteo Manica, Komminist Weldemariam
To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains.
no code implementations • 12 Sep 2022 • Girmaw Abebe Tadesse, Oliver Bent, Komminist Weldemariam, Md. Abrar Istiak, Taufiq Hasan, Andrea Cavallaro
Body-worn first-person vision (FPV) camera enables to extract a rich source of information on the environment from the subject's viewpoint.
no code implementations • 26 May 2021 • Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III, Komminist Weldemariam
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise.
no code implementations • 31 Mar 2021 • Girmaw Abebe Tadesse, Hamza Javed, Yong liu, Jin Liu, Jiyan Chen, Komminist Weldemariam, Tingting Zhu
We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level.
no code implementations • 25 Nov 2020 • Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Komminist Weldemariam
Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approaches.
no code implementations • 9 Nov 2019 • Reginald Bryant, Celia Cintas, Isaac Wambugu, Andrew Kinai, Komminist Weldemariam
Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models.
no code implementations • 9 Nov 2019 • Samuel C. Maina, Reginald E. Bryant, William O. Goal, Robert-Florian Samoilescu, Kush R. Varshney, Komminist Weldemariam
Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset.
no code implementations • 19 Oct 2018 • Skyler Speakman, Srihari Sridharan, Sekou Remy, Komminist Weldemariam, Edward McFowland
This is the first work to introduce "Subset Scanning" methods from the anomalous pattern detection domain to the task of detecting anomalous input of neural networks.