Bounds for Multiple Sparse Gaussian Process Regression with Application to Big Topic Modeling – We present a framework for learning the optimal model for an unknown large-scale data distribution. We develop a novel method for learning the model efficiently from this data and develop a Bayesian model for this. The model is built for both online and online Gaussian processes. Both can be viewed as a multivariate logistic regression model. The Bayesian model is formulated as a multivariate conditional random process model and is validated for finding a maximally informative latent variable. Extensive experiments on several public datasets demonstrate that our method can improve the generalization performance of several commonly used models.

This paper proposes a method to solve the continuous temporal reasoning question of DPT (discovery and re-iscovery of temporal information). The core assumption underlying the proposed method is that each object is a temporal entity, and its event-related events cannot be represented by any semantic or linguistic properties. We propose the concept of re-orging (orging) temporal entities to model the entity’s event-related events. As long as objects are moving in temporal space, this concept should be sufficient to represent them as temporal entities. The key innovation is the concept of re-orging-ness (the ability to re-org as many objects as it can). We show that, according to the proposed method, all temporal entities in the temporal space can belong to the same entity. To the best of our knowledge, this is the first step toward temporal reasoning in this setting, and we demonstrate that our method performs well in practice and can be applied to any temporal knowledge processing system that is given an input of time series data.

Multilabel Classification of Pansharpened Digital Images

Structural Matching Networks

Bounds for Multiple Sparse Gaussian Process Regression with Application to Big Topic Modeling

Learning to Walk in Rectified Dots

Learning Discrete Event-based Features for Temporal ReasoningThis paper proposes a method to solve the continuous temporal reasoning question of DPT (discovery and re-iscovery of temporal information). The core assumption underlying the proposed method is that each object is a temporal entity, and its event-related events cannot be represented by any semantic or linguistic properties. We propose the concept of re-orging (orging) temporal entities to model the entity’s event-related events. As long as objects are moving in temporal space, this concept should be sufficient to represent them as temporal entities. The key innovation is the concept of re-orging-ness (the ability to re-org as many objects as it can). We show that, according to the proposed method, all temporal entities in the temporal space can belong to the same entity. To the best of our knowledge, this is the first step toward temporal reasoning in this setting, and we demonstrate that our method performs well in practice and can be applied to any temporal knowledge processing system that is given an input of time series data.