Παρακολούθηση
Nathan Hodas
Nathan Hodas
Pacific Northwest National Laboratory
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα pnnl.gov
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Deep learning for computational chemistry
GB Goh, NO Hodas, A Vishnu
Journal of computational chemistry 38 (16), 1291-1307, 2017
9332017
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
4932019
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
4522017
The Simple Rules of Social Contagion
N Hodas, K Lerman
Scientific Reports 4 (4343), 2014
3632014
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
2792017
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
2072018
How limited visibility and divided attention constrain social contagion
NO Hodas
SocialCom 2012, 2012
205*2012
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
1932017
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
1452013
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
1092018
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
1002017
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
952019
Asymmetry in RNA pseudoknots: observation and theory
DP Aalberts, NO Hodas
Nucleic acids research 33 (7), 2210-2214, 2005
702005
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
692018
Deep learning experiments for tropical cyclone intensity forecasts
W Xu, K Balaguru, A August, N Lalo, N Hodas, M DeMaria, D Judi
Weather and Forecasting 36 (4), 1453-1470, 2021
662021
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
452018
Metric-based few-shot learning for video action recognition
C Careaga, B Hutchinson, N Hodas, L Phillips
arXiv preprint arXiv:1909.09602, 2019
332019
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
332014
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
302015
Doing the impossible: Why neural networks can be trained at all
NO Hodas, P Stinis
Frontiers in psychology 9, 1185, 2018
282018
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