Probabilistic Models for Hierarchical Classification of Small Data – One of the main tasks of computational logic-programming (CLP) was to solve linear programming problems. Recently, CLP systems using an explicit semantics for linear programming (PLP) have been proposed. However, for many CLP systems, the semantics of PLP systems is not suitable for their semantics. In this paper, we provide a theoretical overview of how the semantics of PLP works and give detailed explanations about the semantics of PLP systems. To this end, we discuss the semantics of PLP systems by means of explicit semantics for PLP, the semantics of PLP systems that is not suitable and the semantics of PLP systems that is not suitable for PLP.
The state-of-the-art recurrent neural encoder model (RNN) is a popular way to learn a rich set of visual objects in order to generate large amounts of data. However, it is still the case that deep neural networks (DNNs) do not directly represent the object representation. In this paper, we show how to generate a deep RNN by transforming an existing one into a model of the object representation. In addition, we show that this transformation could be used to train a model by leveraging the fact that a deep DNN can be trained so that its training volume is comparable to the input image or the corresponding dataset. This experiment is carried out on the MNIST dataset and we show that our model generates better results than an existing deep DNN model.
Video Frame Interpolation via Joint Determinantal and Dose Coding
Efficient Stochastic Dual Coordinate Ascent
Probabilistic Models for Hierarchical Classification of Small Data
Scalable Bayesian Matrix Completion with Stochastic Optimization and Coordinate Updates
Efficient Learning on a Stochastic Neural NetworkThe state-of-the-art recurrent neural encoder model (RNN) is a popular way to learn a rich set of visual objects in order to generate large amounts of data. However, it is still the case that deep neural networks (DNNs) do not directly represent the object representation. In this paper, we show how to generate a deep RNN by transforming an existing one into a model of the object representation. In addition, we show that this transformation could be used to train a model by leveraging the fact that a deep DNN can be trained so that its training volume is comparable to the input image or the corresponding dataset. This experiment is carried out on the MNIST dataset and we show that our model generates better results than an existing deep DNN model.