| Machine learning conservation laws from differential equations Z Liu, V Madhavan, M Tegmark Physical Review E 106 (4), 045307, 2022 | 26 | 2022 |
| Team enigma at ArgMining-EMNLP 2021: Leveraging pre-trained language models for key point matching MN Kapadnis, S Patnaik, SS Panigrahi, V Madhavan, A Nandy arXiv preprint arXiv:2110.12370, 2021 | 8 | 2021 |
| AI Poincaré 2.0: machine learning conservation laws from differential equations Z Liu, V Madhavan, M Tegmark arXiv e-prints, arXiv: 2203.12610, 2022 | 5 | 2022 |
| City-scale simulation of COVID-19 pandemic & intervention policies using agent-based modelling G Suryawanshi, V Madhavan, A Mitra, PP Chakrabarti 2021 Winter Simulation Conference (WSC), 1-12, 2021 | 3 | 2021 |
| Chisquarex at textgraphs 2020 shared task: Leveraging pretrained language models for explanation regeneration AG Pawate, V Madhavan, D Chandak Proceedings of the Graph-based Methods for Natural Language Processing …, 2020 | 3 | 2020 |
| Deep learning-based spatially explicit emulation of an agent-based simulator for pandemic in a city V Madhavan, A Mitra, PP Chakrabarti arXiv preprint arXiv:2205.14396, 2022 | | 2022 |
| Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction V Madhavan, AG Pawate, S Pal, A Chandra arXiv preprint arXiv:2110.04475, 2021 | | 2021 |
| Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching M Nitin Kapadnis, S Patnaik, S Smarak Panigrahi, V Madhavan, A Nandy arXiv e-prints, arXiv: 2110.12370, 2021 | | 2021 |
