The unsung hero of Vector Database -- Metric Learning and Self-Supervised learning





The unsung Hero of vector database. And I am talking about a machine learning concept not one of the companies (because there are so many) 1. Metric learning: What is metric learning? 2. Problem without metric learning, mostly with an example of negation. Cosine similarity doesn't work with negation. 3. How to train metric learning embeddings. 4. Data, Model, and the loss function 5. Data, what is anchor, positive and negative 6. Model: Siamese networks, since we deal with different data, which needs different architecture. 7. Loss: the loss function is big, triple loss, and contrastive loss. 5. Demo of how it improved the overall experience of working with negations.

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Sonam is the creator of the open-source library called Embed-Anything, which helps to create local and multimodal embeddings. She worked previously at Qdrant engine, in RAG, and before that, she worked at Rasa in conversational AI and generative. Previously, she worked as an AI researcher at Saama and has worked extensively on clinical trial analytics with Pfizer. She is passionate about topics like Biases in language models. She has also published a paper in the most reputed journal of computational linguistics, COLING, in ACL Anthology.

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Section: Artificial Intelligence and Machine Learning
Type: Talk
Target Audience: Advanced
Last Updated: