159 citations · 180 across the 13 of their papers we have counts for
13 papers
Domain-agnostic and Multi-level Evaluation of Generative Models
Girmaw Abebe Tadesse, Jannis Born, Celia Cintas +4
While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplifi…
BON: An extended public domain dataset for human activity recognition
Girmaw Abebe Tadesse, Oliver Bent, Komminist Weldemariam +3
Body-worn first-person vision (FPV) camera enables to extract a rich source of information on the environment from the subject's viewpoint. However, the research progress in wearab…
Pattern Detection in the Activation Space for Identifying Synthesized Content
Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse +3
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize…
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
Girmaw Abebe Tadesse, Hamza Javed, Yong Liu +4
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enabl…
Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR
Campbell D. Watson, Chulin Wang, Timothy Lynar +1
In an effort to provide optimal inputs to downstream modeling systems (e.g., a hydrodynamics model that simulates the water circulation of a lake), we hereby strive to enhance reso…
Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys
Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman +1
Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approach…
Multi-factor authentication for users of non-internet based applications of blockchain-based platforms
Andrew Kinai, Fred Otieno, Nelson Bore +1
Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-fac…
ADW: Blockchain-enabled Small-scale Farm Digitization
Nelson Bore, Andrew Kinai, Peninah Waweru +5
Farm records hold the static, temporal, and longitudinal details of the farms. For small-scale farming, the ability to accurately capture these records plays a critical role in for…
Preservation of Anomalous Subgroups On Machine Learning Transformed Data
Samuel C. Maina, Reginald E. Bryant, William O. Goal +3
In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the mos…
Analyzing Bias in Sensitive Personal Information Used to Train Financial Models
Reginald Bryant, Celia Cintas, Isaac Wambugu +2
Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can resul…
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
Daniel Omeiza, Skyler Speakman, Celia Cintas +1
Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outputs have been a concern to computer vision researchers a…
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network
Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus +8
Policy decisions are increasingly dependent on the outcomes of simulations and/or machine learning models. The ability to share and interact with these outcomes is relevant across…
Subset Scanning Over Neural Network Activations
Skyler Speakman, Srihari Sridharan, Sekou Remy +2
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomal…