Learning Better Representations Using Auxiliary Knowledge
Keywords:Knowledge Graph Embedding, Representation Learning, Auxiliary Knowledge, Robustness
AbstractRepresentation Learning is the core of Machine Learning and Artificial Intelligence as it summarizes input data points into low dimensional vectors. This low dimensional vectors should be accurate portrayals of the input data, thus it is crucial to find the most effective and robust representation possible for given input as the performance of the ML task is dependent on the resulting representations. In this summary, we discuss an approach to augment representation learning which relies on external knowledge. We briefly describe the shortcoming of the existing techniques and describe how an auxiliary knowledge source could result in obtaining improved representations.
How to Cite
Rezayi, S. (2023). Learning Better Representations Using Auxiliary Knowledge. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16133-16134. https://doi.org/10.1609/aaai.v37i13.26927
AAAI Doctoral Consortium Track