It is a great pleasure and honor for everyone in Gatorsense that one of our labmates has achieved his goal. Congratulations to Dr. Connor McCurley for graduating with his Ph.D.! Connor’s dissertation is titled “Discriminative Feature Learning with Imprecise, Uncertain, and… Read More
Tag: deep learning
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification accepted to GRSL, 2022!
Congratulations to our labmates and collaborators: Joshua Peeples, Sarah Walker, Connor McCurley, Alina Zare, James Keller and Weihuang Xu! Their paper, “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification”, was recently accepted to IEEE Geoscience and Remote Sensing… Read More
Congratulations to Yiming Cui for a Successful Proposal Defense!
Congratulations to our labmate Yiming Cui for successfully defending his research proposal! Defending an oral research proposal is the second of four milestones to completing a Ph.D. at the University of Florida. Yiming is planning to conduct point cloud semantic… Read More
Histogram Layers For Texture Analysis
Abstract: We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed histogram layer directly computes the spatial… Read More
WALKER PRESENTS AT UF 2021 UNDERGRADUATE RESEARCH SYMPOSIUM!
Congratulations to our labmate, Sarah Walker! Sarah presented her work, titled “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification” at UF’s 2021 Undergraduate Research Virtual Symposium. The virtual symposium featured outstanding undergraduate researchers across all colleges at UF.… Read More
DIVERGENCE REGULATED ENCODER NETWORK FOR JOINT DIMENSIONALITY REDUCTION AND CLASSIFICATION
Abstract: In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples… Read More
EXPLAINABLE SAS ACCEPTED TO IGARSS!
Congratulations to our labmates: Sarah Walker, Joshua Peeples, Jeff Dale, James Keller and Alina Zare! Their paper, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their… Read More
EXPLAINABLE SYSTEMATIC ANALYSIS FOR SYNTHETIC APERTURE SONAR IMAGERY
Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar… Read More
A REMOTE SENSING DERIVED DATA SET OF 100 MILLION INDIVIDUAL TREE CROWNS FOR THE NATIONAL ECOLOGICAL OBSERVATORY NETWORK
Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote… Read More
NULL SPACE ANALYSIS OF NEURAL NETWORKS PRESENTED AT ICML
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