Search Results for author:
Found 6 papers, 4 papers with code
Fine-Grained Zero-Shot Learning with DNA as Side Information
Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.
Open Set Authorship Attribution toward Demystifying Victorian Periodicals
Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration.
Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero and NE Syrtis
A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery.
Bayesian Nonparametrics for Non-exhaustive Learning
Non-exhaustive learning (NEL) is an emerging machine-learning paradigm designed to confront the challenge of non-stationary environments characterized by anon-exhaustive training sets lacking full information about the available classes. Unlike traditional supervised learning that relies on fixed models, NEL utilizes self-adjusting machine learning to better accommodate the non-stationary nature of the real-world problem, which is at the root of many recently discovered limitations of deep learning.
Bayesian Zero-Shot Learning
Object classes that surround us have a natural tendency to emerge at varying levels of abstraction.
The Infinite Mixture of Infinite Gaussian Mixtures
Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems.
