Lambda Learner: Fast Incremental Learning on Data Streams

1 code implementation • 11 Oct 2020 • Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee, Kirill Talanine, Onkar Dalal, Gungor Polatkan, Sara Smoot, Deepak Kumar

One of the most well-established applications of machine learning is in deciding what content to show website visitors.

BIG-bench Machine Learning Incremental Learning

An Attentive Survey of Attention Models

no code implementations • 5 Apr 2019 • Sneha Chaudhari, Varun Mithal, Gungor Polatkan, Rohan Ramanath

This survey provides a structured and comprehensive overview of the developments in modeling attention.

Towards Deep and Representation Learning for Talent Search at LinkedIn

no code implementations • 17 Sep 2018 • Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik

In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.

Learning Semantic Representations Learning-To-Rank +1

Deploying Deep Ranking Models for Search Verticals

no code implementations • 6 Jun 2018 • Rohan Ramanath, Gungor Polatkan, Liqin Xu, Harold Lee, Bo Hu, Shan Zhou

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 deploy to a real-world production system serving 500M+users.

BIG-bench Machine Learning Semantic Similarity +1

Painting Analysis Using Wavelets and Probabilistic Topic Models

no code implementations • 26 Jan 2014 • Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank

In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone.

Clustering Topic Models