| Deep learning for computational chemistry GB Goh, NO Hodas, A Vishnu Journal of computational chemistry 38 (16), 1291-1307, 2017 | 648 | 2017 |
| Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter S Volkova, K Shaffer, JY Jang, N Hodas Proceedings of the 55th annual meeting of the association for computational …, 2017 | 334 | 2017 |
| Learning deep neural network representations for Koopman operators of nonlinear dynamical systems E Yeung, S Kundu, N Hodas 2019 American Control Conference (ACC), 4832-4839, 2019 | 306 | 2019 |
| The Simple Rules of Social Contagion N Hodas, K Lerman Scientific Reports 4 (4343), 2014 | 284 | 2014 |
| Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models GB Goh, C Siegel, A Vishnu, NO Hodas, N Baker arXiv preprint arXiv:1706.06689, 2017 | 190 | 2017 |
| How limited visibility and divided attention constrain social contagion NO Hodas SocialCom 2012, 2012 | 190* | 2012 |
| Few-shot learning with metric-agnostic conditional embeddings N Hilliard, L Phillips, S Howland, A Yankov, CD Corley, NO Hodas arXiv preprint arXiv:1802.04376, 2018 | 154 | 2018 |
| Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties GB Goh, NO Hodas, C Siegel, A Vishnu arXiv preprint arXiv:1712.02034, 2017 | 123 | 2017 |
| Friendship paradox redux: Your friends are more interesting than you N Hodas, F Kooti, K Lerman Proceedings of the International AAAI Conference on Web and Social Media 7 …, 2013 | 119 | 2013 |
| Using rule-based labels for weak supervised learning: a ChemNet for transferable chemical property prediction GB Goh, C Siegel, A Vishnu, N Hodas Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 71 | 2018 |
| Asymmetry in RNA pseudoknots: observation and theory DP Aalberts, NO Hodas Nucleic acids research 33 (7), 2210-2214, 2005 | 71 | 2005 |
| Using social media to predict the future: a systematic literature review L Phillips, C Dowling, K Shaffer, N Hodas, S Volkova arXiv preprint arXiv:1706.06134, 2017 | 70 | 2017 |
| How much chemistry does a deep neural network need to know to make accurate predictions? GB Goh, C Siegel, A Vishnu, N Hodas, N Baker 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1340-1349, 2018 | 54 | 2018 |
| Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples SM Colby, JR Nuñez, NO Hodas, CD Corley, RR Renslow Analytical chemistry 92 (2), 1720-1729, 2019 | 51 | 2019 |
| A koopman operator approach for computing and balancing gramians for discrete time nonlinear systems E Yeung, Z Liu, NO Hodas 2018 Annual American Control Conference (ACC), 337-344, 2018 | 36 | 2018 |
| Network weirdness: Exploring the origins of network paradoxes F Kooti, N Hodas, K Lerman Proceedings of the International AAAI Conference on Web and Social Media 8 …, 2014 | 32 | 2014 |
| Shapeshop: Towards understanding deep learning representations via interactive experimentation F Hohman, N Hodas, DH Chau Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors …, 2017 | 28 | 2017 |
| Disentangling the Lexicons of Disaster Response in Twitter NO Hodas, G Ver Steeg, J Harrison, S Chikkagoudar, E Bell, CD Corley Proceedings of the 24th International Conference on World Wide Web, 2015 | 27 | 2015 |
| Efficient computation of optimal oligo–RNA binding NO Hodas, DP Aalberts Nucleic acids research 32 (22), 6636-6642, 2004 | 24 | 2004 |
| Metric-based few-shot learning for video action recognition C Careaga, B Hutchinson, N Hodas, L Phillips arXiv preprint arXiv:1909.09602, 2019 | 23 | 2019 |
Garrett B. GohML Applied Sciences ManagerVerified email at microsoft.com
