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Learning Discriminative Representations for Word Sense Descriptions with a Multi-task CNN – Learning a word embedding is an important issue in natural language processing (NLP). We have devised a new, powerful, and effective word embedding algorithm for the task of natural language processing. This algorithm uses an external vector representation of the embedding space to […]

A Novel Approach for Estimating the Reproducing Tawnee Crow’s Meal Size Using the Graph Matching Technique – In this paper, we propose a novel approach to estimating the female reproductive system. The main objective is to design a model that can predict the reproductive system. Such a model is based on a novel technique and […]

Multi-dimensional representation learning for word retrieval – We present a novel method for generating sentence-level sentences by applying the recently-developed word embeddings to the sentence embedding network which combines word embeddings with a deep recurrent neural network. We train these deep recurrent neural network models on an image corpus where we learn to model the […]

Convex Similarity Estimation Using the Statistical Basis – Many applications of regression are concerned with the reconstruction of data. In this paper, we propose an approach to estimate the model parameters from the data. The resulting regression can be efficiently done by a Gaussian process or a random process such as stochastic gradient descent. The […]

Rethinking the word-event classification: state of the art, future directions, and future directions away – This paper presents a novel, multi-task, neural-network based algorithm with the ability to learn a sequence of variables. With the ability to model a sequence of variables as a sequence of events, neural networks are able to predict the trajectory […]

The Evolution-Based Loss Functions for Deep Neural Network Training – The deep neural network (CNN) plays a key role in many industrial and non-commercial applications through the use of reinforcement learning (RL). However, the RL is very time consuming. Learning algorithms or deep neural networks are used for the RL tasks. In this paper, we […]

Bayesian Sparse Dictionary Learning – We propose a new method for machine learning. As a consequence, the learning algorithm can learn to encode complex knowledge representations in finite time. We show that the proposed method works with a limited number of parameters and achieves high performance when trained on a standard benchmark dataset. The performance […]

Fast, Simple and Accurate Verification of AdaBoost Trees – We present the first parallel approach to AdaBoost parsing which has the same semantics as AdaBoost (but is based on a deep algorithm). Unlike AdaBoost, our approach is simple and simple. We have implemented a simple approach on the C++ version of the AdaBoost tree which […]

Semi-Supervised Learning Using Randomized Regression – We present a novel learning-based clustering method for hierarchical clustering, called M-LDA, designed to tackle the problem of large-scale sequential clustering based on binary matrix factorization, the clustering problem in computational biology. M-LDA is motivated by the need to deal with large-scale sequential clustering in many different dimension. More […]

Learning and Visualizing the Construction of Visual Features from Image Data – Most of the existing methods use a linear classifier for image classification. In this paper, we present a new approach for image classification by maximizing the expected class error rate of the linear classifier when a linear classifier is used for classifying images […]