Search Results for author: Rohan Sukumaran

Found 15 papers, 4 papers with code

Balancing Act: Constraining Disparate Impact in Sparse Models

2 code implementations31 Oct 2023 Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, Jose Gallego-Posada

Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities.

Omega: Optimistic EMA Gradients

1 code implementation13 Jun 2023 Juan Ramirez, Rohan Sukumaran, Quentin Bertrand, Gauthier Gidel

Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training.

Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss

no code implementations12 Nov 2021 Debapriya Tula, Shreyas Ms, Viswanatha Reddy, Pranjal Sahu, Sumanth Doddapaneni, Prathyush Potluri, Rohan Sukumaran, Parth Patwa

To summarize, our model can handle offensive language detection in a low-resource, class imbalanced, multilingual and code-mixed setting.

Can Self Reported Symptoms Predict Daily COVID-19 Cases?

1 code implementation18 May 2021 Parth Patwa, Viswanatha Reddy, Rohan Sukumaran, Sethuraman TV, Eptehal Nashnoush, Sheshank Shankar, Rishemjit Kaur, Abhishek Singh, Ramesh Raskar

The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states.

Improved Customer Transaction Classification using Semi-Supervised Knowledge Distillation

no code implementations15 Feb 2021 Rohan Sukumaran

Further, using an ALBERT model (it has 33x fewer parameters vis-a-vis parameters of RoBERTa), with RoBERTa as the Teacher, we see a performance similar to that of RoBERTa and better performance over unadapted ALBERT.

Classification General Classification +1

COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

no code implementations21 Dec 2020 Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, Ramesh Raskar

In this study, we understand trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as an alternative to COVID-19 testing reports.

Time Series Forecasting

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