no code implementations • 12 Apr 2024 • Girmaw Abebe Tadesse, Caleb Robinson, Gilles Quentin Hacheme, Akram Zaytar, Rahul Dodhia, Tsering Wangyal Shawa, Juan M. Lavista Ferres, Emmanuel H. Kreike
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes.
no code implementations • 13 Jan 2024 • Gilles Quentin Hacheme, Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson, Rahul Dodhia, Juan M. Lavista Ferres, Stephen Wood
We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method.
no code implementations • 13 Dec 2023 • Tanya Akumu, Celia Cintas, Girmaw Abebe Tadesse, Adebayo Oshingbesan, Skyler Speakman, Edward McFowland III
The representations of the activation space of deep neural networks (DNNs) are widely utilized for tasks like natural language processing, anomaly detection and speech recognition.
no code implementations • 21 Nov 2023 • Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science.
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 • 8 Mar 2022 • Girmaw Abebe Tadesse, William Ogallo, Celia Cintas, Skyler Speakman
Existing feature selection techniques for tabular data often involve fitting a particular model in order to select important features.
no code implementations • 1 Mar 2022 • Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
no code implementations • 6 Jan 2022 • Girmaw Abebe Tadesse, William Ogallo, Catherine Wanjiru, Charles Wachira, Isaiah Onando Mulang', Vibha Anand, Aisha Walcott-Bryant, Skyler Speakman
However, there is a common lack of a principled and scalable feature selection method for efficient discovery.
no code implementations • 23 Nov 2021 • Isaiah Onando Mulang', William Ogallo, Girmaw Abebe Tadesse, Aisha Walcott-Bryant
Analyzing the behaviour of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require specialized attention, but also to assist in designing future interventions.
no code implementations • 5 Nov 2021 • Catherine Wanjiru, William Ogallo, Girmaw Abebe Tadesse, Charles Wachira, Isaiah Onando Mulang', Aisha Walcott-Bryant
The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features.
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 • 24 May 2021 • Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Kush Varshney
Current skin disease models could make incorrect inferences for test samples from different hardware devices and clinical settings or unknown disease samples, which are out-of-distribution (OOD) from the training samples.
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 • 10 Dec 2019 • Girmaw Abebe Tadesse, Tingting Zhu, Nhan Le Nguyen Thanh, Nguyen Thanh Hung, Ha Thi Hai Duong, Truong Huu Khanh, Pham Van Quang, Duc Duong Tran, LamMinh Yen, H Rogier Van Doorn, Nguyen Van Hao, John Prince, Hamza Javed, DaniKiyasseh, Le Van Tan, Louise Thwaites, David A. Clifton
A support vector machine is employed to classify the ANSD levels.