A multivariate adaptive gradient algorithm with reduced tuning efforts.

Large neural networks usually perform well for executing machine learning tasks. However, models that achieve state-of-the-art performance involve arbitrarily large number of parameters and therefore their training is very expensive. It is thus desired to implement methods with small per-iteration costs, fast convergence rates, and reduced tuning.

Learning From Multiple Imperfect Instructors in Sensor Networks.

This paper presents a sequential learning framework for sensors in a network, where a few sensors assume the role of an instructor to train other sensors in the network. The instructors provide estimated labels for measurements of new sensors. These labels are possibly noisy, because a classifier of the instructor may not be perfect.

Non-negative sparse autoencoder neural networks for the detection of overlapping, hierarchical communities in networked datasets.

We propose the first use of a non-negative sparse autoencoder (NNSAE) neural network for community structure detection in complex networks. The NNSAE learns a compressed representation of a set of fixed-length, weighted random walks over the network, and communities are detected as subsets of network nodes corresponding to non-negligible elements of the basis vectors of this compression. The NNSAE model is efficient and online.

Probabilistic color matching and tracking of human subjects.

Pattern discovery algorithms based on the computational mechanics (CM) method have been shown to succinctly describe underlying patterns in data through the reconstruction of minimum probabilistic finite state automata (PFSA). We apply the CM approach toward the tracking of human subjects in real time by matching and tracking the underlying color pattern as observed from a fixed camera. Objects are extracted from a video sequence, and then raster scanned, decomposed with a one-dimensional Haar wavelet transform, and symbolized with the aid of a red-green-blue (RGB) color cube.