| 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 | 183 | 2021 |
| PROSPECT: a system for screening candidates for recruitment A Singh, C Rose, K Visweswariah, V Chenthamarakshan, N Kambhatla Proceedings of the 19th ACM international conference on Information and …, 2010 | 143 | 2010 |
| Fairness GAN: Generating datasets with fairness properties using a generative adversarial network P Sattigeri, SC Hoffman, V Chenthamarakshan, KR Varshney Proc. ICLR Workshop Safe Mach. Learn 2, 2019 | 126 | 2019 |
| 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 |
| 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 | 81* | 2022 |
| Protein Representation Learning by Geometric Structure Pretraining Z Zhang, M Xu, A Jamasb, V Chenthamarakshan, A Lozano, P Das, ... ICLR 2023, 2022 | 78 | 2022 |
| Fairness GAN P Sattigeri, SC Hoffman, V Chenthamarakshan, KR Varshney arXiv preprint arXiv:1805.09910, 2018 | 56 | 2018 |
| Optimizing molecules using efficient queries from property evaluations SC Hoffman, V Chenthamarakshan, K Wadhawan, PY Chen, P Das Nature Machine Intelligence, 1-11, 2021 | 53 | 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 | 52 | 2018 |
| Predicting employee expertise for talent management in the enterprise KR Varshney, V Chenthamarakshan, SW Fancher, J Wang, D Fang, ... Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 51 | 2014 |
| Syntax based reordering with automatically derived rules for improved statistical machine translation K Visweswariah, J Navrátil, J Sorensen, V Chenthamarakshan, ... Proceedings of the 23rd International Conference on Computational …, 2010 | 49 | 2010 |
| Method for an efficient electronic messaging system SO Agrawal, VE Chenthamarakshan, EWB Dlas US Patent 7,890,591, 2011 | 39 | 2011 |
| Rule based synonyms for entity extraction from noisy text R Ananthanarayanan, V Chenthamarakshan, PM Deshpande, ... Proceedings of the second workshop on Analytics for noisy unstructured text …, 2008 | 39 | 2008 |
| Concept labeling: Building text classifiers with minimal supervision V Chenthamarakshan, P Melville, V Sindhwani, RD Lawrence Proceedings of the Twenty-Second international joint conference on …, 2011 | 37 | 2011 |
| System and method for domain adaption with partial observation VE Chenthamarakshan, RD Lawrence, Y Liu, D Zhang US Patent 8,856,052, 2014 | 32 | 2014 |
| Matching candidates with positions based on historical assignment data VE Chenthamarakshan, N Kambhatla, RC Kanjiranthinkal, AKR Singh, ... US Patent App. 13/596,817, 2012 | 26* | 2012 |
| SYSTEMS AND METHODS FOR MATCHING CANDIDATES WITH POSITIONS BASED ON HISTORICAL ASSIGNMENT DATA VE Chenthamarakshan, N Kambhatla, RC Kanjiranthinkal, AKR Singh, ... US Patent App. 12/944,868, 2010 | 24 | 2010 |
| Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design Y Cao, P Das, V Chenthamarakshan, PY Chen, I Melnyk, Y Shen International Conference on Machine Learning, 1261-1271, 2021 | 22 | 2021 |
| System and method for domain adaption with partial observation VE Chenthamarakshan, RD Lawrence, Y Liu, D Zhang US Patent 8,856,050, 2014 | 22 | 2014 |
| System and method for domain adaption with partial observation VE Chenthamarakshan, RD Lawrence, Y Liu, D Zhang US Patent 8,856,050, 2014 | 22 | 2014 |
Inkit PadhiIBM ResearchVerified email at ibm.com
