We propose a method for learning using a set of feature representations which retrieve different amounts of information at different costs. The goal is to create a more efficient ...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene an...
Jake Porway, Kristy Wang, Benjamin Yao, Song Chun ...
Authentic practice in science requires access to phenomena. In this paper, we introduce RoomQuake, an application designed to foster the growth of a community of learning around s...
This paper presents a model of neural network embodiment of intentions and planning mechanisms for autonomous agents. The model bridges the dichotomy of symbolic and non-symbolic ...
— We introduce a graph-based relational learning approach using graph-rewriting rules for temporal and structural analysis of biological networks changing over time. The analysis...