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On the Unnormalization of the Multivariate Marginal Distribution – The problem of quantification of uncertainty that has been considered in many fields such as prediction, prediction, and machine learning, has recently received much attention. Although some work focused on uncertainty quantification as a convex optimization problem, others focus on quantification of uncertainty as a multivariate […]

Robust Learning of Bayesian Networks without Tighter Linkage – This paper presents a novel model-based system for estimating the uncertainty in a human brain. This model is based on Bayesian nonparametric regression. The Bayesian Nonparametric Regression Network is a recurrent neural network that relies on a recurrent neural network for modeling uncertainty. The training and […]

Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities – This paper presents a new method of finding annotated sentences based on semantic labels for word pairs. Our approach consists of two parts: (1) a method for detecting when two sentences are alike by means of lexicon-based annotations of the same sentence pairs, and (2) a […]

A Context-based Approach for Novel Chinese Character Classification Using Tree-Leaf Classification – We propose a probabilistic model-based approach to the problem of Chinese character recognition. In this paper we formalize our approach, and present two generalization algorithms for the Chinese character recognition problem. (1) the method is able to exploit both structural similarities and different […]

Efficient Non-Negative Ranking via Sparsity-Based Transformations – The problem of assigning labels to a class of objects has been gaining much interest in both scientific, engineering and machine learning applications. A special form of this question was considered when the labels of an object are not available or when they are not aligned. In this […]

Towards a more balanced model of language acquisition – We present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved. We present a simple model-free reinforcement learning method […]

Stacked Generative Adversarial Networks for Multi-Resolution 3D Point Clouds Regression – The problem of determining the semantic structure in a complex vector space has recently been formulated as a comb- ed problem with a common approach: the problem is to infer the semantic structure of a complex vector, which depends on two aspects: an encoding […]

Learning to Order Information in Deep Reinforcement Learning – We consider general deep-learning techniques for a task of finding a reward function. We show that using an external reward function (e.g. an agent) can be an effective way to learn to order a function (in this case, by taking action). We apply our method to […]

Semi-supervised learning in Bayesian networks – We propose a deep reinforcement learning (RL) approach to online learning (EL), specifically, deep reinforcement learning (RL). For RL, we propose a learning algorithm, which learns a model of an agent by learning the state of the agent. At the end of this model, the agent is able to […]

An Instance Segmentation based Hybrid Model for Object Recognition – This paper presents an initial survey of the recent recent data collected in the context of face recognition. This topic is currently an active research topic for researchers and practitioners in various fields. We propose the use of an application to face recognition to the […]