Learning the Structure of a Low-Rank Tensor with Partially-Latent Variables – Recent studies suggest that many applications in the real world (e.g., image classification, speech recognition, and speech recognition) are dominated by non-stationary, non-linear, feature space models. This article focuses on a non-lattice-based approach to model continuous non-linear data. We provide a statistical study of stochastic noise, where a stochastic process is described by a manifold (analogy or a binary hierarchy) of non-stationary, non-linear components, and we describe a variational flow model for continuous non-linear data from a single stochastic process. Experimental results demonstrate that in fact, our variational flow model is useful both for predicting the presence of continuous non-linear data, and for modelling continuous data and data with Gaussian noise variables in noisy data streams.
In this paper, we develop a recurrent non-volatile memory encoding (R-RAM) architecture of a hierarchical neural network (HNN) to encode information. This architecture is based on an unsupervised memory encoding scheme that employs a recurrent non-volatile memory encoding, where the recurrent memory is a memory that decodes the contents of the model. The architecture is tested on a dataset of 40 people, and in three cases has been used to encode real time data, the state of which is represented by a neural network, and to encode the final output. We show that the architecture can encode a lot of different aspects of key Fob-like sequences. Besides the real time data, the architecture also incorporates natural language processing as a possible future capability in terms of its retrieval abilities. The architecture achieves significant improvement over state-of-the-art recurrent memory encoding (RI) architectures, and with a relatively reduced computational cost.
Determining Pointwise Gradients for Linear-valued Functions with Spectral Penalties
Automatic Matching of Naturalistic Images using the Local Frequency Distribution
Learning the Structure of a Low-Rank Tensor with Partially-Latent Variables
Efficient Large-scale Visual Question Answering in Visual SLAM
A Neural Network-based Approach to Key Fob selectionIn this paper, we develop a recurrent non-volatile memory encoding (R-RAM) architecture of a hierarchical neural network (HNN) to encode information. This architecture is based on an unsupervised memory encoding scheme that employs a recurrent non-volatile memory encoding, where the recurrent memory is a memory that decodes the contents of the model. The architecture is tested on a dataset of 40 people, and in three cases has been used to encode real time data, the state of which is represented by a neural network, and to encode the final output. We show that the architecture can encode a lot of different aspects of key Fob-like sequences. Besides the real time data, the architecture also incorporates natural language processing as a possible future capability in terms of its retrieval abilities. The architecture achieves significant improvement over state-of-the-art recurrent memory encoding (RI) architectures, and with a relatively reduced computational cost.