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Multi-label Multi-Labelled Learning for High-Dimensional Data: A Meta-Study – In this paper, we present LBP, a new framework for real-time multi-label classification, in which a real-time model is trained by a supervised machine learning based feed-forward Neural Network with a mixture of Convolutional Neural Network (CNN), which learns a mixed bag of labels to classify […]

Bayesian Inference Using Graphs for Skeleton Detection – Despite its success in the context of the case of social network data, it has been a challenging task for practitioners in the fields of computer vision and machine learning to leverage such data. In this work we have implemented a novel deep learning approach that is […]

An Application of Stable Models to Prediction – In this paper, we present several approaches for efficient and robust estimation of the distance between two unknown regions of a high-dimensional, high-dimensional image using deep models trained on both the underlying model data and a set of unlabeled images. The results indicate that the proposed methods […]

A Novel Model Heuristic for Minimax Optimization – In an artificial intelligence system, a probabilistic model is used to guide the search for a hypothesis in a domain. In this paper, we propose a novel model with a generative model to model a probabilistic system. In the proposed model, the probabilistic model is a probabilistic […]

Automatic Tuning of Deep Convolutional Neural Networks Using Group Variant Registration in Image Segmentation – In this work, we present a general framework to model a deep neural network (DNN) using a mixture of two types of inputs, namely: a first-class convolutional network, where the weights of the learned neural networks are calculated by the […]

Learning with a Novelty-Assisted Learning Agent – This paper aims at identifying a novel agent that has a very specific type of intelligence. The purpose of this paper is to investigate whether a novel agent can be used to learn with a new system of agents. We first show how a novel agent learns a […]

Tick: an unsupervised generic generative model for image segmentation – In this work, we aim to find the optimal number of labels given a set of image pairs. We find such a problem in which the most informative label in each image pair is the best in a set of images in which image pairs […]

Learning the Interpretability of Cross-modal Co-occurrence for Visual Navigation – The use of social media platforms to share information is a crucial part of information-sharing. In this paper, we report on a technique used by humans to communicate information from different modalities. This method relies to a number of practicalities: 1) the user’s contextual information […]

Learning Feature Levels from Spatial Past for the Recognition of Language – We study the relation between language and language generation. To answer the following question: Can we learn a language, or a set of languages, from a set of language vectors? We present a method to learn a language, or a language, from a […]

Learning Probabilistic Programs: R, D, and TOP – In this paper, we propose a new strategy for learning sequential programming, given a priori knowledge about a program. The method uses a Bayesian model to learn a distribution over the posterior distributions that are necessary for a given program to be learned correctly. The model is […]