We explore a knowledge sanitization approach to mitigate the privacy con...
This study employs Independent Component Analysis (ICA) to uncover unive...
The main objective of Knowledge Graph (KG) embeddings is to learn
low-di...
Distributed representations of words encode lexical semantic information...
Measuring the semantic similarity between two sentences is still an impo...
For supervised classification problems, this paper considers estimating ...
It is well-known that typical word embedding methods such as Word2Vec an...
Measuring preferential attachment in growing networks is an important to...
Multimodal relational data analysis has become of increasing importance ...
k-nearest neighbour (k-NN) is one of the simplest and most widely-used
m...
Refining one's hypotheses in the light of data is a commonplace scientif...
We propose a statistical method to estimate simultaneously the non-param...
A collection of U (∈N) data vectors is called a U-tuple,
and the assoc...
A general resampling approach is considered for selective inference prob...
We propose weighted inner product similarity (WIPS) for
neural-network b...
We propose β-graph embedding for robustly learning feature vectors from
...
Statistical inference is considered for variables of interest, called pr...
Selective inference is considered for testing trees and edges in phyloge...
We propose shifted inner-product similarity (SIPS), which is a novel yet...
Applying conventional word embedding models to unsegmented languages, wh...
The representation power of similarity functions used in neural network-...
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is
p...
Selective inference procedures are considered for computing approximatel...
The strength of association between a pair of data vectors is represente...
Data vectors are obtained from multiple domains. They are feature vector...
Structural equation models and Bayesian networks have been widely used t...