We present a novel extension of the traditional neural network approach ...
Large language models appear to learn facts from the large text corpora ...
In principle, applying variational autoencoders (VAEs) to sequential dat...
Representing words by vectors, or embeddings, enables computational reas...
As data volumes continue to grow, the labelling process increasingly bec...
Much of biomedical and healthcare data is encoded in discrete, symbolic ...
In semi-supervised learning (SSL), a rule to predict labels y for data x...
We present network embedding algorithms that capture information about a...
Many methods have been developed to represent knowledge graph data, whic...
Hyperbolic embeddings have recently gained attention in machine learning...
Word embeddings generated by neural network methods such as word2vec (W2...
Knowledge graphs are structured representations of real world facts. How...
Knowledge graphs are large graph-structured databases of facts, which
ty...
Vector representation, or embedding, of words is commonly achieved with
...