We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fid...
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation...