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Found 16 papers, 0 papers with code
Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes.
Weak Labeling for Cropland Mapping in Africa
We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method.
Efficient Representation of the Activation Space in Deep Neural Networks
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.
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
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.
Domain-agnostic and Multi-level Evaluation of Generative Models
To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains.
BON: An extended public domain dataset for human activity recognition
Body-worn first-person vision (FPV) camera enables to extract a rich source of information on the environment from the subject's viewpoint.
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data
Existing feature selection techniques for tabular data often involve fitting a particular model in order to select important features.
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
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.
Sparsity-based Feature Selection for Anomalous Subgroup Discovery
However, there is a common lack of a principled and scalable feature selection method for efficient discovery.
Post-discovery Analysis of Anomalous Subsets
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.
Automated Supervised Feature Selection for Differentiated Patterns of Care
The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features.
Pattern Detection in the Activation Space for Identifying Synthesized Content
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise.
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning
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.
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
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.
Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys
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.
Severity Detection Tool for Patients with Infectious Disease
A support vector machine is employed to classify the ANSD levels.
