Publications
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Application of complex split-activation feedforward networks to beamforming
Summary
Summary
In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real...
Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates
Summary
Summary
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...
Development of a field artifical intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds
Summary
Summary
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in...
Health-informed policy gradients for multi-agent reinforcement learning
Summary
Summary
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...
Multimodal representation learning via maximization of local mutual information [e-print]
Summary
Summary
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image...
Learning emergent discrete message communication for cooperative reinforcement learning
Summary
Summary
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...
Beyond expertise and roles: a framework to characterize the stakeholders of interpretable machine learning and their needs
Summary
Summary
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples...
Ultrasound diagnosis of COVID-19: robustness and explainability
Summary
Summary
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an...
Ultrasound and artificial intelligence
Summary
Summary
Compared to other major medical imaging modalities such as X-ray, computed tomography (CT), and magnetic resonance imaging, medical ultrasound (US) has unique attributes that make it the preferred modality for many clinical applications. In particular, US is nonionizing, portable, and provides real-time imaging, with adequate spatial and depth resolution to...
Failure prediction by confidence estimation of uncertainty-aware Dirichlet networks
Summary
Summary
Reliably assessing model confidence in deep learning and predicting errors likely to be made are key elements in providing safety for model deployment, in particular for applications with dire consequences. In this paper, it is first shown that uncertainty-aware deep Dirichlet neural networks provide an improved separation between the confidence...