An Approach for Language Modeling in Prescription, Part 1: The Keywords – In this paper we present a formal approach to learn a machine translation approach for word embedding. The word embedding problem is motivated by the task of representing natural language, which has the capability of capturing the full meaning of words. In this paper, we propose a new approach that considers the embedding capacity of a word, in terms of the size of the input vector. We also propose an efficient method to learn the neural embedding, called Multi-Target Neural Embedding (MTNE). The MTL-2 approach uses recurrent neural networks, which are trained on this dataset. The key features of the MTL-2 approach are: (a) it adaptively learns to extract the embedding capacity of a word; (b) it can take different embedding capacities during training by varying the weights of the embedding capacity; (c) it takes different embedding capacities during training, by training different neural network models with different embedding capacities. The MTL-2 approach outperforms the previous state-of-the-art in terms of word embedding accuracy and retrieval throughput on the MNIST data sets.
This paper analyzes and describes a technique called Multi-Person Identification (MNI) that leverages a new type of neural architecture called Multi-Person Sparse Attention Networks (MAP-AUNs). MAP-AUNs allow to combine two sets of parts: the part that encodes information about the people in each other’s visual world, and the part that directly performs actions for that specific person. MAP-AUNs are trained simultaneously and trained using an input input that describes the person’s activities in his world. The network’s architecture then is used to perform the action that the person is currently doing.
Person re-identification (re-ID) is a vital and essential task in many areas of life. The most important challenges come from the different types of re-ID data. In this paper, we address the data quality issue of unstructured re-ID, based on multiple sets of multi-level features. This work aims at reducing the data clutter by using two types of features: multiple-objective features and the multilayer perceptron (MOT).
Distributed Stochastic Gradient with Variance Bracket Subsampling
A hybrid linear-time-difference-converter for learning the linear regression of structured networks
An Approach for Language Modeling in Prescription, Part 1: The Keywords
Probabilistic Models for Hierarchical Classification of Small Data
A Unified Approach to Multi-Person Identification and Movement Identification using Partially-Occurrence Multilayer NetworksThis paper analyzes and describes a technique called Multi-Person Identification (MNI) that leverages a new type of neural architecture called Multi-Person Sparse Attention Networks (MAP-AUNs). MAP-AUNs allow to combine two sets of parts: the part that encodes information about the people in each other’s visual world, and the part that directly performs actions for that specific person. MAP-AUNs are trained simultaneously and trained using an input input that describes the person’s activities in his world. The network’s architecture then is used to perform the action that the person is currently doing.
Person re-identification (re-ID) is a vital and essential task in many areas of life. The most important challenges come from the different types of re-ID data. In this paper, we address the data quality issue of unstructured re-ID, based on multiple sets of multi-level features. This work aims at reducing the data clutter by using two types of features: multiple-objective features and the multilayer perceptron (MOT).