| Does String-Based Neural MT Learn Source Syntax? X Shi, I Padhi, K Knight Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 366 | 2016 |
| Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations P Das, T Sercu, K Wadhawan, I Padhi, S Gehrmann, F Cipcigan, ... Nature Biomedical Engineering 5 (6), 613-623, 2021 | 182 | 2021 |
| Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer CN Santos, I Melnyk, I Padhi Proceedings of ACL 2018, P18-2031, 2018 | 154 | 2018 |
| Cogmol: Target-specific and selective drug design for covid-19 using deep generative models V Chenthamarakshan, P Das, S Hoffman, H Strobelt, I Padhi, KW Lim, ... Advances in Neural Information Processing Systems 33, 2020 | 92 | 2020 |
| Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text N Madaan, I Padhi, N Panwar, D Saha Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 2020 | 65 | 2020 |
| Large-scale chemical language representations capture molecular structure and properties J Ross, B Belgodere, V Chenthamarakshan, I Padhi, Y Mroueh, P Das Nature Machine Intelligence 4 (12), 1256-1264, 2022 | 64 | 2022 |
| Tabular Transformers for Modeling Multivariate Time Series I Padhi, Y Schiff, I Melnyk, M Rigotti, Y Mroueh, P Dognin, J Ross, R Nair, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 59 | 2021 |
| PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences P Das, K Wadhawan, O Chang, T Sercu, CD Santos, M Riemer, I Padhi, ... arXiv preprint arXiv:1810.07743, 2018 | 51 | 2018 |
| Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge P Dognin, I Melnyk, Y Mroueh, I Padhi, M Rigotti, J Ross, Y Schiff, ... Journal of Artificial Intelligence Research 73, 437-459, 2022 | 34 | 2022 |
| Improved Neural Text Attribute Transfer with Non-parallel Data I Melnyk, CN Santos, K Wadhawan, I Padhi, A Kumar NIPS 2017 Workshop on Learning Disentangled Representations: from Perception …, 2017 | 25 | 2017 |
| Learning Implicit Generative Models by Matching Perceptual Features CN Santos, Y Mroueh, I Padhi, P Dognin Proceedings of the IEEE International Conference on Computer Vision, 4461-4470, 2019 | 18 | 2019 |
| Do large scale molecular language representations capture important structural information? J Ross, B Belgodere, V Chenthamarakshan, I Padhi, Y Mroueh, P Das arXiv e-prints, arXiv: 2106.09553, 2021 | 15 | 2021 |
| DualTKB: A Dual Learning Bridge between Text and Knowledge Base PL Dognin, I Melnyk, I Padhi, CN Santos, P Das EMNLP 2020, 2020 | 14 | 2020 |
| Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim V Chenthamarakshan arXiv preprint arXiv:2004.01215, 2020 | 14 | 2020 |
| ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models PL Dognin, I Padhi, I Melnyk, P Das arXiv preprint arXiv:2108.12472, 2021 | 11 | 2021 |
| GT4SD: Generative Toolkit for Scientific Discovery M Manica, J Cadow, D Christofidellis, A Dave, J Born, D Clarke, ... arXiv preprint arXiv:2207.03928, 2022 | 10 | 2022 |
| Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting P Sattigeri, S Ghosh, I Padhi, P Dognin, KR Varshney Annual Conference on Neural Information Processing Systems, 2022 | 8 | 2022 |
| The Impact of Positional Encoding on Length Generalization in Transformers A Kazemnejad, I Padhi, KN Ramamurthy, P Das, S Reddy arXiv preprint arXiv:2305.19466, 2023 | 6 | 2023 |
| Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations (vol 5, pg 613, 2021) P Das, T Sercu, K Wadhawan, I Padhi, S Gehrmann, F Cipcigan, ... NATURE BIOMEDICAL ENGINEERING 5 (8), 942-942, 2021 | 6* | 2021 |
| Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection T Sercu, S Gehrmann, H Strobelt, P Das, I Padhi, C Dos Santos, ... | 5 | 2019 |
