We introduce an active learning framework designed to train classification models which use informative projections. Our approach works with the obtained lowdimensional models in...
We present a probabilistic approach to shape decomposition that creates a skeleton-based shape representation of a 3D object while simultaneously decomposing it into constituent p...
Tarek El-Gaaly, Vicky Froyen, Ahmed M. Elgammal, J...
Utilizing attributes for visual recognition has attracted increasingly interest because attributes can effectively bridge the semantic gap between low-level visual features and hi...
This paper presents the first functional evaluation of spontaneous, uncued retrieval from long-term memory in a cognitive architecture. The key insight is that current deliberate...
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a fo...
In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modaliti...
We present a probabilistic framework for learning with heterogeneous multiview data where some views are given as ordinal, binary, or real-valued feature matrices, and some views ...
In this paper we describe asprin1 , a general, flexible, and extensible framework for handling preferences among the stable models of a logic program. We show how complex prefere...
Gerhard Brewka, James P. Delgrande, Javier Romero ...
Spectral clustering, a graph partitioning technique, has gained immense popularity in machine learning in the context of unsupervised learning. This is due to convincing empirical...
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Policy gradient algorithms, which directl...