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… Read More
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TREE CROWNS DATASET NOW AVAILABLE!
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… Read More
NEON TREE CROWNS DATASET
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… Read More
OUTLIER DETECTION THROUGH NULL SPACE ANALYSIS OF NEURAL NETWORKS
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… Read More
PLANTS MEET MACHINES PUBLISHED IN APPLICATIONS IN PLANT SCIENCES!
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… Read More
PLANTS MEET MACHINES: PROSPECTS IN MACHINE LEARNING FOR PLANT BIOLOGY
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… Read More
ROOT IDENTIFICATION WITH MULTIPLE INSTANCE LEARNING PUBLISHED IN MACHINE VISION AND APPLICATIONS!
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!… Read More
SPICE IS NOW AVAILABLE IN ANACONDA!
Sparsity Promoting Iterated Constrained Endmemebers (SPICE) is now installable with conda! SPICE is an algorithm for finding hyperspectral endmembers and corresponding proportions for a scene. The Python implementation can now be installed easily from PyPI or through the conda-forge. Installation… Read More
ENSEMBLE CORRDET ACCEPTED TO IET SMART GRID!
Congratulations to our labmates and collaborators, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Surya Dhulipala, Allen Starke, Arturo Bretas, Alina Zare , and Janise McNair! Their paper, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” was accepted to IET Smart Grid. … Read More
ENSEMBLE CORRDET WITH ADAPTIVE STATISTICS FOR BAD DATA DETECTION
Abstract: Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing numbers of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad… Read More