Publication

PREDICTIVE MODELS TO IDENTIFY HOLSTEIN COWS AT RISK OF METRITIS AND CLINICAL CURE AND REPRODUCTIVE/PRODUCTIVE FAILURE FOLLOWING ANTIMICROBIAL TREATMENT

Abstract: Precision dairy farming, specifically the design of management strategies according to the animal’s needs, may soon become the norm since automated technologies that generate large amounts of data for each individual are becoming more affordable. Our objectives were to… Read More

ADDRESSING ANNOTATION IMPRECISION FOR TREE CROWN DELINEATION USING THE RANDCROWNS INDEX

Abstract: Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when the targets are of irregular shape or difficult to distinguish from the background or neighboring objects. Tree… Read More

CLASSIFICATION WITH MULTI-IMPRECISE LABELS

Abstract: Imprecise labels or label uncertainty are common problems in many real supervised and semi-supervised learning problems. However, most of the state-of-the-art supervised learning methods in the literature rely on accurate labels. Accurate labels are often either expensive, time-consuming, or… Read More

NON-INVASIVE HEART RATE ESTIMATION FROM BALLISTOCARDIOGRAMS USING BIDIRECTIONAL LSTM REGRESSION

Abstract: Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The… 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 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