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ORCIDNathan Oken Hodas
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- affiliation: Pacific Northwest National Laboratory, Richland, WA, USA
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Journal Articles
- 2018
[j2]Nathan O. Hodas, Jacob S. Hunter, Stephen J. Young
, Kristina Lerman:
Model of cognitive dynamics predicts performance on standardized tests. J. Comput. Soc. Sci. 1(2): 295-312 (2018)- 2017
- [j1]Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu:
Deep learning for computational chemistry. J. Comput. Chem. 38(16): 1291-1307 (2017)
Conference and Workshop Papers
- 2021
- [c16]Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas:
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. MetaDL@AAAI 2021: 77-89 - 2019
- [c15]Enoch Yeung, Soumya Kundu, Nathan O. Hodas:
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. ACC 2019: 4832-4839 - 2018
- [c14]Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas:
A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. ACC 2018: 337-344 - [c13]Meg Pirrung, Nathan Hilliard, Nancy O'Brien, Artëm Yankov, Court D. Corley, Nathan O. Hodas:
SHARKZOR: Human in the Loop ML for User-Defined Image Classification. IUI Companion 2018: 29:1-29:2 - [c12]Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan Oken Hodas:
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction. KDD 2018: 302-310 - [c11]Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan A. Baker:
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? WACV 2018: 1340-1349 - 2017
- [c10]Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, Nathan Oken Hodas:
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. ACL (2) 2017: 647-653 - [c9]Fred Hohman, Nathan O. Hodas, Duen Horng Chau:
ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. CHI Extended Abstracts 2017: 1694-1699 - [c8]Lawrence Phillips, Nathan Oken Hodas:
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. CogSci 2017 - [c7]Lawrence Phillips, Kyle Shaffer, Dustin Arendt, Nathan Oken Hodas, Svitlana Volkova:
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams. Rep4NLP@ACL 2017: 201-210 - 2016
- [c6]Nathan O. Hodas, Ryan Butner, Court D. Corley:
How a User's Personality Influences Content Engagement in Social Media. SocInfo (1) 2016: 481-493 - 2015
- [c5]Nathan O. Hodas, Greg Ver Steeg, Joshua J. Harrison, Satish Chikkagoudar, Eric Bell, Courtney D. Corley:
Disentangling the Lexicons of Disaster Response in Twitter. WWW (Companion Volume) 2015: 1201-1204 - 2014
- [c4]Farshad Kooti, Nathan Oken Hodas, Kristina Lerman:
Network Weirdness: Exploring the Origins of Network Paradoxes. ICWSM 2014 - 2013
- [c3]Nathan Oken Hodas, Kristina Lerman:
Attention and visibility in an information-rich world. ICME Workshops 2013: 1-6 - [c2]Nathan Oken Hodas, Farshad Kooti, Kristina Lerman:
Friendship Paradox Redux: Your Friends Are More Interesting Than You. ICWSM 2013 - 2012
- [c1]Nathan Oken Hodas, Kristina Lerman:
How Visibility and Divided Attention Constrain Social Contagion. SocialCom/PASSAT 2012: 249-257
Informal and Other Publications
- 2021
- [i35]Elliott Skomski, Aaron Tuor, Andrew Avila, Lauren A. Phillips, Zachary New, Henry Kvinge, Courtney D. Corley, Nathan O. Hodas:
Prototypical Region Proposal Networks for Few-Shot Localization and Classification. CoRR abs/2104.03496 (2021) - [i34]Henry Kvinge, Scott Howland, Nico Courts, Lauren A. Phillips, John Buckheit, Zachary New, Elliott Skomski, Jung H. Lee, Sandeep Tiwari, Jessica Hibler, Courtney D. Corley, Nathan O. Hodas:
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. CoRR abs/2106.01423 (2021) - [i33]Jung H. Lee, Henry J. Kvinge, Scott Howland, Zachary New, John Buckheit, Lauren A. Phillips, Elliott Skomski, Jessica Hibler, Courtney D. Corley, Nathan O. Hodas:
Adaptive Transfer Learning: a simple but effective transfer learning. CoRR abs/2111.10937 (2021) - 2020
- [i32]Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas:
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. CoRR abs/2009.11253 (2020) - 2019
- [i31]Chris Careaga, Brian Hutchinson, Nathan Oken Hodas, Lawrence Phillips:
Metric-Based Few-Shot Learning for Video Action Recognition. CoRR abs/1909.09602 (2019) - [i30]Lawrence Phillips, Garrett B. Goh, Nathan O. Hodas:
Explanatory Masks for Neural Network Interpretability. CoRR abs/1911.06876 (2019) - 2018
- [i29]Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, Nathan O. Hodas:
Few-Shot Learning with Metric-Agnostic Conditional Embeddings. CoRR abs/1802.04376 (2018) - [i28]Meg Pirrung, Nathan Hilliard, Artëm Yankov, Nancy O'Brien, Paul Weidert, Courtney D. Corley, Nathan O. Hodas:
Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary. CoRR abs/1802.05316 (2018) - [i27]Nathan O. Hodas, Panos Stinis:
Doing the impossible: Why neural networks can be trained at all. CoRR abs/1805.04928 (2018) - [i26]Nathan O. Hodas, Jacob S. Hunter, Stephen J. Young, Kristina Lerman:
Model of Cognitive Dynamics Predicts Performance on Standardized Tests. CoRR abs/1809.02647 (2018) - [i25]Craig Bakker, Michael J. Henry, Nathan O. Hodas:
The Outer Product Structure of Neural Network Derivatives. CoRR abs/1810.03798 (2018) - 2017
- [i24]Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu:
Deep Learning for Computational Chemistry. CoRR abs/1701.04503 (2017) - [i23]Lyndsey Franklin, Kristina Lerman, Nathan Oken Hodas:
Will Break for Productivity: Generalized Symptoms of Cognitive Depletion. CoRR abs/1706.01521 (2017) - [i22]Lyndsey Franklin, Nathan Oken Hodas:
Cognitive Depletion in the Wild: a Case Study of NMR Spectroscopy Analysis. CoRR abs/1706.01523 (2017) - [i21]Lawrence Phillips, Nathan Oken Hodas:
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. CoRR abs/1706.01839 (2017) - [i20]Lyndsey Franklin, Kyungsik Han, Zhuanyi Huang, Dustin Arendt, Nathan Oken Hodas:
Understanding Cognitive Depletion in Novice NMR Analysts. CoRR abs/1706.01919 (2017) - [i19]Lawrence Phillips, Chase Dowling, Kyle Shaffer, Nathan Oken Hodas, Svitlana Volkova:
Using Social Media to Predict the Future: A Systematic Literature Review. CoRR abs/1706.06134 (2017) - [i18]Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan Oken Hodas, Nathan A. Baker:
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models. CoRR abs/1706.06689 (2017) - [i17]Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley:
Dynamic Input Structure and Network Assembly for Few-Shot Learning. CoRR abs/1708.06819 (2017) - [i16]Enoch Yeung, Soumya Kundu, Nathan O. Hodas:
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. CoRR abs/1708.06850 (2017) - [i15]Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas:
A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. CoRR abs/1709.08712 (2017) - [i14]Kristina Lerman, Nathan O. Hodas, Hao Wu:
Bounded Rationality in Scholarly Knowledge Discovery. CoRR abs/1710.00269 (2017) - [i13]Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan A. Baker:
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? CoRR abs/1710.02238 (2017) - [i12]Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu:
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. CoRR abs/1712.02034 (2017) - [i11]Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas:
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction. CoRR abs/1712.02734 (2017) - 2016
- [i10]Nathan Oken Hodas, Alex Endert:
Adding Semantic Information into Data Models by Learning Domain Expertise from User Interaction. CoRR abs/1604.02935 (2016) - [i9]Nathan O. Hodas, Ryan Butner, Courtney D. Corley:
How a user's personality influences content engagement in social media. CoRR abs/1609.00108 (2016) - [i8]Ark Anderson, Kyle Shaffer, Artëm Yankov, Courtney D. Corley, Nathan Oken Hodas:
Beyond Fine Tuning: A Modular Approach to Learning on Small Data. CoRR abs/1611.01714 (2016) - [i7]Jacob S. Hunter, Nathan O. Hodas:
Mutual information for fitting deep nonlinear models. CoRR abs/1612.05708 (2016) - 2014
- [i6]Farshad Kooti, Nathan Oken Hodas, Kristina Lerman:
Network Weirdness: Exploring the Origins of Network Paradoxes. CoRR abs/1403.7242 (2014) - 2013
- [i5]Nathan Oken Hodas, Farshad Kooti, Kristina Lerman:
Friendship Paradox Redux: Your Friends Are More Interesting Than You. CoRR abs/1304.3480 (2013) - [i4]Nathan Oken Hodas, Kristina Lerman:
Attention and Visibility in an Information Rich World. CoRR abs/1307.4798 (2013) - [i3]Nathan Oken Hodas, Kristina Lerman:
The Simple Rules of Social Contagion. CoRR abs/1308.5015 (2013) - 2012
- [i2]Nathan Oken Hodas, Kristina Lerman:
How Visibility and Divided Attention Constrain Social Contagion. CoRR abs/1205.2736 (2012) - 2010
- [i1]Nathan O. Hodas:
The Quality of Oscillations in Overdamped Networks. CoRR abs/1006.0271 (2010)
Coauthor Index
aka: Court D. Corley
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