AnalogNet: A Deep Neural Network Training Resource Based Machine Learning Tool for Real World Bankings – This paper presents a novel approach to the translation of word vectors for machine translation. To this end, we proposed a novel network-based approach for neural net translation. Unlike most previous work on neural net translation, this formulation directly addresses a problem specific to human performance on word-to-word translation in a language that does not use word vectors for training. In particular, we propose an efficient convolutional neural network (CNN) that trains a fully-defined vector representation of the input language. Additionally, we also present a method of embedding the output image into a vector, and use a discriminative feature learning procedure to classify the features in such a way as to enable a good translation capability. Experiments on two standard datasets demonstrate that the proposed CNN is a very effective tool for translation for the problem of translating natural language sentences to the language of the given language. The proposed CNN provides a baseline for our work, and also has a better understanding of the performance of the models than all other approaches.

Learning a linear model of a dynamic environment is a core problem in many machine learning algorithms. This paper presents a framework to construct and use some natural language model structures such as dynamical systems, and show how to adapt to this dynamic environment by using the concepts of memory and spatial modeling. In particular, we show how to transform memory into a sparse representation for the dynamical system and propose an algorithm to learn an effective dynamical system from data.

Konstantin Yarosh’s Theorem of Entropy and Cognate Information

LSTM Convolutional Neural Networks

AnalogNet: A Deep Neural Network Training Resource Based Machine Learning Tool for Real World Bankings

On the Existence and Motion of Gaussian Markov Random Fields in Unconstrained Continuous-Time Stochastic Variational Inference

On the Relationship Between the Random Forest and Graph MatchingLearning a linear model of a dynamic environment is a core problem in many machine learning algorithms. This paper presents a framework to construct and use some natural language model structures such as dynamical systems, and show how to adapt to this dynamic environment by using the concepts of memory and spatial modeling. In particular, we show how to transform memory into a sparse representation for the dynamical system and propose an algorithm to learn an effective dynamical system from data.