no code implementations • EMNLP 2021 • Vivek Madan, Ashish Khetan, Zohar Karnin
In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training.
no code implementations • 23 May 2022 • Anastasia Razdaibiedina, Ashish Khetan, Zohar Karnin, Daniel Khashabi, Vishaal Kapoor, Vivek Madan
In this paper, we propose Representation Projection Invariance (REPINA), a novel regularization method to maintain the information content of representation and reduce representation collapse during fine-tuning by discouraging undesirable changes in the representations.
no code implementations • 23 Jul 2021 • Fred Qin, Vivek Madan, Ujjwal Ratan, Zohar Karnin, Vishaal Kapoor, Parminder Bhatia, Taha Kass-Hout
Clinical text provides essential information to estimate the severity of the sepsis in addition to structured clinical data.
no code implementations • 1 Jan 2021 • Vivek Madan, Ashish Khetan, Zohar Karnin
The need for such a method is clear as it is infeasible to collect a large pre-training corpus for every possible domain.
no code implementations • 16 Apr 2020 • Vivek Madan, Aleksandar Nikolov, Mohit Singh, Uthaipon Tantipongpipat
Our main result is a new approximation algorithm with an approximation guarantee that depends only on the dimension $d$ of the vectors and not on the size $k$ of the output set.