Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …

Positional encodings (PEs) are essential for effective graph representation learning because
they provide position awareness in inherently position-agnostic transformer architectures …

Recommendation systems predominantly utilize two-tower architectures, which evaluate
user-item rankings through the inner product of their respective embeddings. However, one …

Predictive tasks on relational databases are critical in real-world applications spanning e-
commerce, healthcare, and social media. To address these tasks effectively, Relational …

Transformer models have continuously expanded into all machine learning domains
convertible to the underlying sequence-to-sequence representation, including tabular data …

The analysis of structure in complex datasets has become essential to solving difficult
Machine Learning problems. Relational aspects of data, capturing relationships between …

Though large language models (LLMs) have demonstrated exceptional performance across
numerous problems, their application to predictive tasks in relational databases remains …

Large general purpose pre-trained models have revolutionized computer vision and natural
language understanding. However, the development of general purpose pre-trained Graph …

Relational databases, organized into tables connected by primary-foreign key relationships,
are widely used in industry. Companies leverage this data to build highly accurate, feature …