12 citations · 19 across the 8 of their papers we have counts for
9 papers
Practical Online KV Cache Compaction for LLM Agents: An Empirical Study
Yujian Liu, Jiabao Ji, Li An +4
LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV cache compaction can reduce t…
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
Hamed Firooz, Maziar Sanjabi, Adrian Englhardt +20
Ranking and recommendation systems are the foundation for numerous online experiences, ranging from search results to personalized content delivery. These systems have evolved into…
Lambda Learner: Fast Incremental Learning on Data Streams
Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee +5
One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generat…
An Attentive Survey of Attention Models
Sneha Chaudhari, Varun Mithal, Gungor Polatkan +1
Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehens…
Towards Deep and Representation Learning for Talent Search at LinkedIn
Rohan Ramanath, Hakan Inan, Gungor Polatkan +6
Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search…
Deploying Deep Ranking Models for Search Verticals
Rohan Ramanath, Gungor Polatkan, Liqin Xu +3
In this paper, we present an architecture executing a complex machine learning model such as a neural network capturing semantic similarity between a query and a document; and depl…