3 accepted papers
Representation 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 p…
Pretraining domain-specific language models remains an important challenge which limits their applicability in various areas such as agriculture. This paper investigates the effectiveness of leveraging food related text corpora (e.g., food and agricultural literature) in pretraining transformer-base…
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the…