1 code implementation • NeurIPS 2021 • Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.
no code implementations • NAACL 2021 • Hrituraj Singh, Anshul Nasery, Denil Mehta, Aishwarya Agarwal, Jatin Lamba, Balaji Vasan Srinivasan
In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.
2 code implementations • Findings (ACL) 2021 • Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
1 code implementation • NeurIPS 2020 • Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin
We present an algorithm that is fast enough to speed up several matrix operations.
1 code implementation • 25 Jun 2020 • Satyam Mohla, Anshul Nasery, Biplab Banerjee
Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme results in models employing Convolutional Neural Networks (CNNs), conflicting with early works claiming that these networks identify objects using shape.