Search Results for author:
Found 12 papers, 0 papers with code
A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
Deep learning approaches require collection of data on many different input features or variables for accurate model training and prediction.
Improving Short Text Classification With Augmented Data Using GPT-3
This study compares two classifiers: the GPT-3 Classification Endpoint with augmented examples, and the GPT-3 Completion Endpoint with an optimal training set chosen using a genetic algorithm.
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images
Inspired by the recent advance in semi-supervised learning and deep learning, we propose mfTacoma to learn alternative deep representations in the context of TMA image scoring.
Estimating the Number of Infected Cases in COVID-19 Pandemic
We propose a structured approach for the estimation of the number of unreported cases, where we distinguish cases that arrive late in the reported numbers and those who had mild or no symptoms and thus were not captured by any medical system at all.
$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Analysis on $DC^2$ when applied to spectral clustering shows that the loss in clustering accuracy due to data division and reduction is upper bounded by the data approximation error which would vanish with recursive random projections.
Similarity Kernel and Clustering via Random Projection Forests
Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains.
Learning over inherently distributed data
This framework only requires a small amount of local signatures to be shared among distributed sites, eliminating the need of having to transmitting big data.
Fast communication-efficient spectral clustering over distributed data
However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead.
Cost-sensitive Selection of Variables by Ensemble of Model Sequences
Many applications require the collection of data on different variables or measurements over many system performance metrics.
K-nearest Neighbor Search by Random Projection Forests
Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.
Spectral Clustering with Perturbed Data
We show that the error under perturbation of spectral clustering is closely related to the perturbation of the eigenvectors of the Laplacian matrix.
