Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks.
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on Short Text Clustering on Stackoverflow
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy
Cryptography and Security
In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain.
In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa).
Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?