Search Results for author: Vivek Madan

Found 5 papers, 0 papers with code

TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection

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.

Anomaly Detection Data Augmentation +1

Representation Projection Invariance Mitigates Representation Collapse

no code implementations23 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.

Improving Early Sepsis Prediction with Multi Modal Learning

no code implementations23 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.

Domain Adaptation via Anaomaly Detection

no code implementations1 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.

Anomaly Detection Domain Adaptation +1

Maximizing Determinants under Matroid Constraints

no code implementations16 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.

Experimental Design

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