Jina Embeddings
Jina embeddings are vector representations of data, primarily text and images, designed to capture semantic meaning and relationships for improved information retrieval and downstream tasks. Current research focuses on enhancing embedding quality through novel loss functions (e.g., SimO loss for fine-grained contrastive learning), developing efficient architectures like decoupled embeddings for handling large datasets and multilingual contexts, and exploring non-Euclidean spaces (e.g., hyperbolic space) to better represent complex relationships. These advancements are improving performance in diverse applications, including recommendation systems, question answering, and even cybersecurity by enabling more accurate similarity searches and more effective model training.
Papers
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Nour Jedidi, Yung-Sung Chuang, Leslie Shing, James Glass
Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules
Md Abdur Rahman, Md Abdul Barek, ABM Kamrul Islam Riad, Md Mostafizur Rahman, Md Bajlur Rashid, Smita Ambedkar, Md Raihan Miaa, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed
Indication Finding: a novel use case for representation learning
Maren Eckhoff, Valmir Selimi, Alexander Aranovitch, Ian Lyons, Emily Briggs, Jennifer Hou, Alex Devereson, Matej Macak, David Champagne, Chris Anagnostopoulos
Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction
Sergio Burdisso, Srikanth Madikeri, Petr Motlicek
On Debiasing Text Embeddings Through Context Injection
Thomas Uriot
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
Hossein Mirzaei, Mackenzie W. Mathis
Dissecting embedding method: learning higher-order structures from data
Liubov Tupikina (UPD5, LPI), Kathuria Hritika (LPI)
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Robert Graf, Florian Hunecke, Soeren Pohl, Matan Atad, Hendrik Moeller, Sophie Starck, Thomas Kroencke, Stefanie Bette, Fabian Bamberg, Tobias Pischon, Thoralf Niendorf, Carsten Schmidt, Johannes C. Paetzold, Daniel Rueckert, Jan S Kirschke