Category: News

Abstract: Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in […]

Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

Today the University of Florida announced a $70 million artificial intelligence partnership with NVIDIA!  The initiative will create an AI-centric data center at UF, housing the fastest AI supercomputer in higher education.  Along with this technology, UF will collaborate with NVIDIA to further advance both research and education in machine learning and artificial intelligence. While […]

Congratulations to our labmates, Matt Cook, Alina Zare and Paul Gader for presenting at the 37th International Conference on Machine Learning (ICML) Workshop on Uncertainty and Robustness in Machine Learning! Their paper, titled “Outlier Detection through Null Space Analysis of Neural Networks”, introduces a novel method for detecting outliers in a set of data. Matt will […]

  We are happy to announce the publication of a new dataset!  The NEON Tree Crowns Dataset is a collection of individual tree crown estimates for 100 million trees from 37 geographic sites across the United States.  This dataset provides predicted bounding boxes, tree heights, crown areas, class labels and confidence scores for images taken from […]

Abstract: The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. Links: Citation: B. Weinstein, S. Marconi, A. Zare, […]

Abstract: Many machine learning classification systems lack competency awareness. Specifically, many systems lack the ability to identify when outliers (e.g., samples that are distinct from and not represented in the training data distribution) are being presented to the system. The ability to detect outliers is of practical significance since it can help the system behave […]

Congratulations to our labmates and collaborators Pamela S. Soltis, Gil Nelson, Alina Zare and Emily K. Meineke!    They wrote the introduction for a special issue of Applicaitons in Plant Sciences.  Their introduction is titled “Plants meet machines: Prospects in machine learning for plant biology”, and it discusses the ways machine learning is being applied […]

Abstract: Machine learning approaches are affecting all aspects of modern society, from autocorrect applications on cell phones to self‐driving cars to facial recognition, personalized medicine, and precision agriculture. Although machine learning has a long history, drastic improvements in these application areas recently have been driven by improvements to computational infrastructure; increased computing power; increased ability […]

Congratulations to our labmates and collaborators Guohao Yu, Alina Zare, Hudanyun Sheng, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently published in Machine Vision and Applications! Their paper explores the use of multiple instance learning to segment minirhizotron images of plant […]