Nathan Hodas
Nathan Hodas
Verified email at pnnl.gov
Title
Cited by
Cited by
Year
Deep learning for computational chemistry
GB Goh, NO Hodas, A Vishnu
Journal of computational chemistry 38 (16), 1291-1307, 2017
3312017
The Simple Rules of Social Contagion
N Hodas, K Lerman
Scientific Reports 4 (4343), 2014
1812014
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
1632017
How limited visibility and divided attention constrain social contagion
NO Hodas
SocialCom 2012, 2012
160*2012
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
1162019
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
1062017
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 (1), 2013
1022013
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
692018
Asymmetry in RNA pseudoknots: observation and theory
DP Aalberts, NO Hodas
Nucleic acids research 33 (7), 2210-2214, 2005
642005
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
522017
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
402017
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
342018
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
332018
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
232015
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
222018
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
212017
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 (1), 2014
202014
Efficient computation of optimal oligo–RNA binding
NO Hodas, DP Aalberts
Nucleic acids research 32 (22), 6636-6642, 2004
202004
Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples
SM Colby, JR Nuņez, NO Hodas, CD Corley, RR Renslow
Analytical chemistry 92 (2), 1720-1729, 2019
182019
Microscopic structure and dynamics of air/water interface by computer simulations—comparison with sum-frequency generation experiments
Y Wang, NO Hodas, Y Jung, RA Marcus
Phys. Chem. Chem. Phys. 13 (12), 5388-5393, 2011
172011
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