3 papers
cs.AI2026
Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling
Hailing Cheng, Daqi Sun, Xinyu Lu
Every Transformer architecture dedicates enormous capacity to learning rich representations in semantic embedding space -- yet the rotation manifold acted upon by Rotary Positional…
cs.LG2025
From Features to Transformers: Redefining Ranking for Scalable Impact
Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14
We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…
cs.LG2024
LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage across LinkedIn
Sudarshan Srinivasa Ramanujam, Akanksha Bindal, Yu Jiang +8
A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces…