1 code implementation • 29 Nov 2022 • Devansh Mehta, Harshita Diddee, Ananya Saxena, Anurag Shukla, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Vishnu Prasad, Venkanna U, Kalika Bali
The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data.
2 code implementations • 10 Nov 2020 • Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma
In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.
no code implementations • LREC 2020 • Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali
The primary obstacle to developing technologies for low-resource languages is the lack of usable data.
7 code implementations • 19 May 2019 • Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.