Retrosynthetic reaction prediction using neural sequence-to-sequence models B Liu, B Ramsundar, P Kawthekar, J Shi, J Gomes, Q Luu Nguyen, S Ho, ... ACS central science 3 (10), 1103-1113, 2017 | 507 | 2017 |
Transfer learning for improving model predictions in highly configurable software P Jamshidi, M Velez, C Kästner, N Siegmund, P Kawthekar 2017 IEEE/ACM 12th International Symposium on Software Engineering for …, 2017 | 125 | 2017 |
Evaluating generative models for text generation P Kawthekar, R Rewari, S Bhooshan URL https://web. stanford. edu/class/archive/cs/cs224n/cs224n 1174, 2017 | 19 | 2017 |
Predicting transcriptional regulatory activities with deep convolutional networks J Paggi, A Lamb, K Tian, I Hsu, PL Cedoz, P Kawthekar BioRxiv, 099879, 2017 | 6 | 2017 |
Sensitivity analysis for building evolving and & adaptive robotic software P Kawthekar, C Kästner Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR) 7, 2016 | 5 | 2016 |
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models, ACS Cent Sci. 3 (2017) 1103–1113 B Liu, B Ramsundar, P Kawthekar, J Shi, J Gomes, QL Nguyen, S Ho, ... | 4 | |
DART: Deep learning for ART M Dumonal, Y He, P Kawthekar | | |