Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

改良

燦爛

Unsupervised Ensemble Learning Based on Graph Embedding for Image Clusterings well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble Learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the .-means method to ob

irreducible

蓋他為秘密

Event Causality Identification by Modeling Events and Relation Embeddingion. Traditional approaches of causality relation identification rely on the recognition of casual relationship connectives or manual features of causality relationships, and these methods have disadvantage of low recognition coverage and being lack of adaptive. To solve this problem, we propose a n

lanugo

似少年

Hybridized Character-Word Embedding for Korean Traditional Document Translationatical patterns. In recent times, a neural network-based machine translation architecture such as sequence-to-sequence (seq2seq) model showed superior performance in translation. However, it suffers out-of-vocabulary (OOV) issue when dealing with very complex and vocabulary languages such as Chinese

擁護者

Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decompositionachine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are opt

個阿姨勾引你

Meta-path Based Heterogeneous Graph Embedding for Music Recommendationtion techniques which are based on conventional collaborative filtering or acoustic content features usually sufffer from data sparsity or time-consuming computation problems, respectively. In fact, online music services not only generate listening history for each user but also accumulate a large a

Intruder

Knowledge Graph Embedding via Entities’ Type Mapping Matrixowever, KG remains incomplete, inconsistent, and not completely accurate. To deal with the challenges of KGs, many state-of-the-art models, such as TransE, TransH, and TransR, have been proposed. TransE and TransH use one semantic space for entities and relations, whereas TransR uses two different s

分發(fā)