Learned Embeddings

Learned embeddings are low-dimensional vector representations of data, aiming to capture semantic relationships and facilitate efficient processing for various tasks. Current research focuses on improving embedding quality through techniques like contrastive learning, attention mechanisms, and novel loss functions (e.g., centroid triplet loss), often within the context of specific model architectures such as transformers and graph neural networks. These advancements are driving progress in diverse fields, including computer vision (object identification, image generation), natural language processing (long-context language modeling, multi-hop reasoning), and recommendation systems, by enabling more accurate and efficient data analysis and downstream applications.