no code implementations • 12 Aug 2021 • Aman Gupta, Rohan Ramanath, Jun Shi, Anika Ramachandran, Sirou Zhou, Mingzhou Zhou, S. Sathiya Keerthi
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.
1 code implementation • 9 Mar 2021 • Rohan Ramanath, S. Sathiya Keerthi, Yao Pan, Konstantin Salomatin, Kinjal Basu
We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes.
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.
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.
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.
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.