30 citations · 34 across the 6 of their papers we have counts for
6 papers
Enhancing Self-Training Methods
Aswathnarayan Radhakrishnan, Jim Davis, Zachary Rabin +3
Semi-supervised learning approaches train on small sets of labeled data along with large sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that of…
Bottom-up Hierarchical Classification Using Confusion-based Logit Compression
Tong Liang, Jim Davis, Roman Ilin
In this work, we propose a method to efficiently compute label posteriors of a base flat classifier in the presence of few validation examples within a bottom-up hierarchical infer…
A Classification Refinement Strategy for Semantic Segmentation
James W. Davis, Christopher Menart, Muhammad Akbar +1
Based on the observation that semantic segmentation errors are partially predictable, we propose a compact formulation using confusion statistics of the trained classifier to refin…
Formal Concept Analysis of Rodent Carriers of Zoonotic Disease
Roman Ilin, Barbara A. Han
The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers,or those species carrying…
Grounded Symbols in the Brain Computational Foundations for Perceptual Symbol System
Leonid Perlovsky, Roman Ilin
We describe a mathematical models of grounded symbols in the brain. It also serves as a computational foundations for Perceptual Symbol System (PSS). This development requires new…
Beyond Feedforward Models Trained by Backpropagation: a Practical Training Tool for a More Efficient Universal Approximator
Roman Ilin, Robert Kozma, Paul J. Werbos
Cellular Simultaneous Recurrent Neural Network (SRN) has been shown to be a function approximator more powerful than the MLP. This means that the complexity of MLP would be prohibi…