NEGATIVE-SAMPLING WORD-EMBEDDING METHOD

One of the most famous authors of the method is Tomas Mikolov. His software and method of theoretical application are the major ones for our consideration today. It is better to pay attention that it is more mathematically oriented. The use of embedding models to turn KGs into vector space has become a well-known field of research. In recent years, a plethora of embedding learning approaches have been proposed in the literature. Many of these models rely on data already stored in the input KG. Following the closed world assumption, the knowledge not presented in the KG cannot be judged untrue; instead, it may only be labeled as unknown. On the other hand, embedding models, like most machine learning algorithms, require negative instances to learn embeddings efficiently. To deal with this, a variety of negative sample generating strategies have been developed. The author himself had more to do with mathematics, and his method concerns, first of all, a mathematical solution for a theoretical, and then a practical solution for creating this and the method we are analyzing. Dense vector word representations have lately gained popularity as fixed-length features for machine learning algorithms, and Mikolov’s system is now widely used. We investigate one of its main components, Negative Sampling, and offer efficient distributed methods that allow us to scale to indicate and exclude the possibility of probability loss in a similar value. Furthermore, this method is laser-focused on a single action in the broad sense for processing the recognition of the above-mentioned vector or words. It is important to pay attention to mathematical theory and understand the importance of the neural network in this field

МЕТОД ВСТРАИВАНИЯ СЛОВ С ОТРИЦАТЕЛЬНОЙ ВЫБОРКОЙ

Одним из самых известных авторов метода является Томас Миколов. Его программное обеспечение и метод теоретического применения являются одними из основных для нашего сегодняшнего рассмотрения. Стоит отметить, что он более математически ориентирован. Сам автор, Томас Миколов, имел больше отношения к математике, и его метод касается прежде всего математического решения для теоретического, а затем практического решения для создания этого и метода, который мы анализируем. Плотные векторные представления слов в последнее время приобрели популярность в качестве функций фиксированной длины для алгоритмов машинного обучения, и система Миколова в настоящее время широко используется. Мы исследуем один из его основных компонентов, отрицательную выборку, и предлагаем эффективные распределенные методы, которые позволяют нам масштабировать, чтобы указать и исключить возможность потери вероятности при аналогичном значении. Говоря о развитии нейронной сети, не стоит забывать о математической базовой теории понимания вероятности выбора слова. Кроме того, этот метод сфокусирован на одном действии в широком смысле для обработки распознавания вышеупомянутого вектора или слов.One of the most famous authors of the method is the person Thomas Mikolov. Its software and method of theoretical application is one of the major ones for our consideration today. It is better to pay attention that it is more mathematically oriented. The author himself had more to do with mathematics and his method concerns first of all a mathematical solution for a theoretical, and then a practical solution for creating this and the method we are analyzing. Dense vector word representations have lately gained popularity as fixed-length features for machine learning algorithms, and Mikolov’s system is now widely used. We investigate one of its main components, Negative Sampling, and offer efficient distributed methods that allow us to scale to indicate and exclude the possibility of probability loss in a similar value. Furthermore, this method is laser-focused on a single action in the broad sense for processing the recognition of the above-mentioned vector or words

End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

Participation Cost Estimation: Private Versus Non-Private Study

In our study, we seek to learn the real-time crowd levels at popular points of interests based on users continually sharing their location data. We evaluate the benefits of users sharing their location data privately and non-privately, and show that suitable privacy-preserving mechanisms provide incentives for user participation in a private study as compared to a non-private study