9 citations · 15 across the 6 of their papers we have counts for
6 papers
DiPietro-Hazari Kappa: A Novel Metric for Assessing Labeling Quality via Annotation
Daniel M. DiPietro, Vivek Hazari
Data is a key component of modern machine learning, but statistics for assessing data label quality remain sparse in literature. Here, we introduce DiPietro-Hazari Kappa, a novel s…
Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale
Daniel DiPietro, Vivek Hazari, Soroush Vosoughi
Suicide is a major public health crisis. With more than 20,000,000 suicide attempts each year, the early detection of suicidal intent has the potential to save hundreds of thousand…
Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems
Daniel M. DiPietro, Bo Zhu
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data. SISR employs a deep symbolic regression…
Quantitative Stopword Generation for Sentiment Analysis via Recursive and Iterative Deletion
Daniel M. DiPietro
Stopwords carry little semantic information and are often removed from text data to reduce dataset size and improve machine learning model performance. Consequently, researchers ha…
Soft Multicopter Control using Neural Dynamics Identification
Yitong Deng, Yaorui Zhang, Xingzhe He +5
Dynamic control of a soft-body robot to deliver complex behaviors with low-dimensional actuation inputs is challenging. In this paper, we present a computational approach to automa…
Sparse Symplectically Integrated Neural Networks
Daniel M. DiPietro, Shiying Xiong, Bo Zhu
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data. SSINNs combine fourth-order symplectic i…