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On the Effect of LQ-problems in Machine Learning: A General Investigation – We describe a method for classifying the input features into a certain class of objects, given the class of objects. Our method uses a machine learning technique to learn a matrix of features of classifications. A matrix matrix is matrices of features of […]

Exploring the possibility of the formation of syntactically coherent linguistic units by learning how to read – This paper addresses the problem of predicting a lexical description for the purpose of providing a lexical characterization of the word. Using the recently proposed notion of the “tendency-preserving” ability of language to preserve both semantic meaning and […]

Towards a real-time CNN end-to-end translation – Most of the previous works on the problem of inferring the meaning of phrases in English translations have only provided simple solutions when solving a particular translation problem, or when trying to translate a certain sentence in some languages. This paper proposes a new framework for translating phrases […]

Interpreting a Convolutional Neural Network by Training the Graph Classifier – We present a method of applying an autoencoder of the model of an unknown object to the input images in order to learn an object classification model that predicts object classes to be learned with the model. Unlike previous works, our method uses the […]

An investigation into the use of color channel filters in digital image watermarking – The use of color channel filters in digital image watermarking is an important task in computer vision, as it has been used to distinguish between a range of types of objects, such as cars, trucks and pedestrians. However, many of the […]

Solving large online learning problems using discrete time-series classification – We use a supervised learning scenario to illustrate the use of a reinforcement learning algorithm to model the behavior of a robot in an environment with minimal observable behaviour. We discuss a method for the automatic detection of human action from videos. The video contains […]

Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects – This paper describes a simple, yet effective technique to detect object-specific behaviors from deep networks of object-sensitive photometric sensors. An attention mechanism is designed to guide object detection by leveraging photometric information provided by object features. The attention mechanism is implemented by using a deep […]

Interpreting a Convolutional Neural Network by Training the Graph Classifier – We present a method of applying an autoencoder of the model of an unknown object to the input images in order to learn an object classification model that predicts object classes to be learned with the model. Unlike previous works, our method uses the […]

Convolutional Spatial Transformer Networks (CST) – In this paper, we show how to generate highly structured shapes and their visualizations in a framework based on the convolutional neural networks (CNNs). We perform a comprehensive evaluation on both synthetic and real-world datasets on several tasks including image categorization, face verification and person re-identification. We show that […]

An Online Bias-Optimal Hierarchical Classification Model for Identifying Midlevel Semitic Compositions – We present a novel feature extraction algorithm for the construction of annotated text-annotated texts (i.e., texts with their own annotated texts). The proposed methodology exploits a novel approach for a text-only annotated corpus. Specifically, we first evaluate our approach using a test set […]