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Using the Multi-dimensional Bilateral Distribution for Textual Discrimination – We present a new dataset for a novel kind of semantic discrimination (tense) task aiming at comparing two types of text: semantic and unsemantically. It includes large-scale annotated hand annotated datasets that are large in size and are capable of covering an entire language. We propose […]

Learning to Learn Discriminatively-Learning Stochastic Grammars – Learning to learn is one of the key challenges of Machine Learning (ML) and Machine Learning (ML), in machine learning. The main problems are to learn the most general (non-negative) samples of the data and the best (positive) samples of the data, and in the latter case to […]

Interpretable Feature Extraction via Hitting Scoring – Deep learning has long been recognized as a powerful learning method. As recently as 2014, when the use of neural networks was being made prominent, the work was still done in the theoretical and practical direction. Since then many applications have been developed in the artificial intelligence field […]

Learning Visual Representations with Convolutional Neural Networks and Binary Networks for Video Classification – We present a simple system that aims to extract images from a video and predict what they will look like from that. We provide a simple algorithm based on a convolutional neural network to automatically learn the pose of the videos […]

Using a Convolutional Restricted Boltzmann Machine to Detect and Track Multiple Targets – This paper addresses the problem of determining the best match between two-way and two-player online strategies. This problem was proposed in the paper’s article ‘Online-Hierarchical Coaching’, which is based on online learning of strategies to minimize regret (or reward) for certain actions, […]

Scalable Data Classification by Exploiting Bayesian Spatial Information – We show that when a model can be transformed to a model, the resulting model can also be classified into several classes with high probability. For example, we show that if $S$ is transformed to $M$ it can be classified into $K$-Class, $T$-Class or even $L$-Class. […]

Deep Learning-Based Action Detection with Recurrent Generative Adversarial Networks – We provide a new way of inferring action predictions in a Bayesian setting. Using this new information, we show that an action prediction can be performed in a Bayesian framework. In particular, we show that a posterior prediction that is an action predictor can be […]

A Closer Look at The Global Structure of Cellular Automata – This paper describes a new method for the identification of a complex set of features in a given data set. The algorithm uses a new algorithm called MultiMiner to detect the similarities between different samples in the data set. It is shown that the […]

Recursive Stochastic Gradient Descent for Nonconvex Stochastic Optimization – We show that the best solution for convex optimization can be obtained if the problem is nonconvex. This is a simple fact but one of a very natural and relevant problem. This problem is one of the most widely studied in the literature. We propose a […]

Graph Construction: The Crossover Operator and the Min-Cost Surrogate Learning – We present a novel approach to optimizing optimal-learning algorithms for nonlinear graphs. Inspired by the well-known approach to graph-level optimization, we solve a variant of this problem to derive a novel, fast, scalable, and efficient greedy algorithm for minimizing the loss of the nonlinear […]