
Texture segmentation by convex relaxation
Texture segmentation by convex relaxation – Turing2.0 is a simple image processing framework to automatically transform pixellevel features into semantic labels of a target image. Our approach uses a monocular convolutional neural network to learn the semantic segmentation function and generate the semantic labels of two frames. We evaluate our approach on both synthetic datasets […]

Toward Accurate Text Recognition via Transfer Learning
Toward Accurate Text Recognition via Transfer Learning – We present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and nonsyntactic information from a corpus. We evaluate our approach using several datasets […]

Learning Graphical Models of Text to Artifacts
Learning Graphical Models of Text to Artifacts – In this work we investigate the problem of using a semantic graph model to represent texts. We first present a graph model that learns to extract semantic relationships given their data. Our approach is based on using a text graph to describe each line of text. Our […]

Possibilistic functions, fuzzy case by Gabor, and fuzzy case by Posen
Possibilistic functions, fuzzy case by Gabor, and fuzzy case by Posen – This paper focuses on fuzzy theorytheoretic framework for solving problems by nonmonotonic functions such as Euclidean geometry. The fuzzy theory, based on the formalism of F.P. Sinyor, and on the notion of Euclidean geometry, has been developed as a generalization of the notion […]

Generating More Reliable Embeddings via Semantic Parsing
Generating More Reliable Embeddings via Semantic Parsing – In this paper, we propose a deep learning framework for automatically transforming a text into its constituent tokens. We first propose a novel and very promising technique based on word level and word alignment rules for wordlevel semantic transformation using syntactic information encoded by semantic relations. From […]

Classification of Brain Areas Using Convolutional Neural Networks
Classification of Brain Areas Using Convolutional Neural Networks – In this paper, we propose an endtoend method for predicting Alzheimer’s Disease (AD) in brain tissue by using unsupervised learning. In this paper, an attention based classifier (ADC) is proposed for Alzheimer’s Disease (DAD) prediction, based on a deep featurebased model which can learn the visual […]

Reconstructing the Human Mind
Reconstructing the Human Mind – Many people tend to consider a new person’s personality type based on how they describe themselves. In this paper, we study why people will rate personality and personality based on features from two related types of the personality. The first two contribute to identifying new personality types: personality type is […]

Stochastic Learning of Nonlinear Partial Differential Equations
Stochastic Learning of Nonlinear Partial Differential Equations – We consider the problem of learning to predict a single highdimensional geometric product of a set of discrete variables, given a set of arbitrary objects. We show that the problem of learning these products can be easily solved. Our approach is an extension of the recentlydeveloped Bayesian […]

Invertibility in Nonconvex Nonconjugate Statistics
Invertibility in Nonconvex Nonconjugate Statistics – Supervised learning has become increasingly popular due to its potential to be used in domains in which the underlying network structure is unknown, and can potentially be exploited for probabilistic reasoning. In nonconvex learning, the problem of optimality is formulated in terms of subspaces: one can learn a policyfree […]

Learning the Structure and Parameters of Large Vocabularies with Linear Context Models
Learning the Structure and Parameters of Large Vocabularies with Linear Context Models – In this paper, we study the problem of modelbased learning of complex sentences, involving both a language description task and language learning tasks. We study the problem of learning a neural network to translate sentences in a language, in a model that […]