The Quality of Oscillations in Overdamped Networks. - Nathan O. Hodas (CoRR, 2010)

How Visibility and Divided Attention Constrain Social Contagion. - Nathan Oken Hodas; Kristina Lerman (SocialCom/PASSAT, 2012)

Attention and visibility in an information-rich world. - Nathan Oken Hodas; Kristina Lerman (ICME Workshops, 2013)

Friendship Paradox Redux: Your Friends Are More Interesting Than You. - Nathan Oken Hodas; Farshad Kooti; Kristina Lerman (ICWSM, 2013)

The Simple Rules of Social Contagion. - Nathan Oken Hodas; Kristina Lerman (CoRR, 2013)

Network Weirdness: Exploring the Origins of Network Paradoxes. - Farshad Kooti; Nathan Oken Hodas; Kristina Lerman (ICWSM, 2014)

Disentangling the Lexicons of Disaster Response in Twitter. - Nathan O. Hodas; Greg Ver Steeg; Joshua J. Harrison; Satish Chikkagoudar; Eric Bell; Courtney D. Corley (WWW (Companion Volume), 2015)

How a User's Personality Influences Content Engagement in Social Media. - Nathan O. Hodas; Ryan Butner; Court D. Corley (SocInfo (1), 2016)

Beyond Fine Tuning: A Modular Approach to Learning on Small Data. - Ark Anderson; Kyle Shaffer; Artëm Yankov; Courtney D. Corley; Nathan Oken Hodas (CoRR, 2016)

Adding Semantic Information into Data Models by Learning Domain Expertise from User Interaction. - Nathan Oken Hodas; Alex Endert (CoRR, 2016)

Mutual information for fitting deep nonlinear models. - Jacob S. Hunter; Nathan O. Hodas (CoRR, 2016)

Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. - Svitlana Volkova; Kyle Shaffer; Jin Yea Jang; Nathan Oken Hodas (ACL (2), 2017)

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. - Fred Hohman; Nathan O. Hodas; Duen Horng Chau (CHI Extended Abstracts, 2017)

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. - Lawrence Phillips; Nathan Oken Hodas (CogSci, 2017)

Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams. - Lawrence Phillips; Kyle Shaffer; Dustin Arendt; Nathan Oken Hodas; Svitlana Volkova (Rep4NLP@ACL, 2017)

Cognitive Depletion in the Wild: a Case Study of NMR Spectroscopy Analysis. - Lyndsey Franklin; Nathan Oken Hodas (CoRR, 2017)

Understanding Cognitive Depletion in Novice NMR Analysts. - Lyndsey Franklin; Kyungsik Han; Zhuanyi Huang; Dustin Arendt; Nathan Oken Hodas (CoRR, 2017)

Will Break for Productivity: Generalized Symptoms of Cognitive Depletion. - Lyndsey Franklin; Kristina Lerman; Nathan Oken Hodas (CoRR, 2017)

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models. - Garrett B. Goh; Charles Siegel; Abhinav Vishnu; Nathan Oken Hodas; Nathan A. Baker (CoRR, 2017)

Using Social Media to Predict the Future: A Systematic Literature Review. - Lawrence Phillips; Chase Dowling; Kyle Shaffer; Nathan Oken Hodas; Svitlana Volkova (CoRR, 2017)

Dynamic Input Structure and Network Assembly for Few-Shot Learning. - Nathan Hilliard; Nathan O. Hodas; Courtney D. Corley (CoRR, 2017)

Bounded Rationality in Scholarly Knowledge Discovery. - Kristina Lerman; Nathan O. Hodas; Hao Wu (CoRR, 2017)

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. - Garrett B. Goh; Nathan O. Hodas; Charles Siegel; Abhinav Vishnu (CoRR, 2017)

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction. - Garrett B. Goh; Charles Siegel; Abhinav Vishnu; Nathan O. Hodas (CoRR, 2017)

Deep learning for computational chemistry. - Garrett B. Goh; Nathan O. Hodas; Abhinav Vishnu (J. Comput. Chem., 2017)

A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. - Enoch Yeung; Zhiyuan Liu 003; Nathan O. Hodas (ACC, 2018)

SHARKZOR: Human in the Loop ML for User-Defined Image Classification. - Meg Pirrung; Nathan Hilliard; Nancy O'Brien; Artëm Yankov; Court D. Corley; Nathan O. Hodas (IUI Companion, 2018)

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction. - Garrett B. Goh; Charles Siegel; Abhinav Vishnu; Nathan Oken Hodas (KDD, 2018)

How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? - Garrett B. Goh; Charles Siegel; Abhinav Vishnu; Nathan O. Hodas; Nathan A. Baker (WACV, 2018)

Few-Shot Learning with Metric-Agnostic Conditional Embeddings. - Nathan Hilliard; Lawrence Phillips; Scott Howland; Artëm Yankov; Courtney D. Corley; Nathan O. Hodas (CoRR, 2018)

Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary. - Meg Pirrung; Nathan Hilliard; Artëm Yankov; Nancy O'Brien; Paul Weidert; Courtney D. Corley; Nathan O. Hodas (CoRR, 2018)

Doing the impossible: Why neural networks can be trained at all. - Nathan O. Hodas; Panos Stinis (CoRR, 2018)

The Outer Product Structure of Neural Network Derivatives. - Craig Bakker; Michael J. Henry; Nathan O. Hodas (CoRR, 2018)

Model of cognitive dynamics predicts performance on standardized tests. - Nathan O. Hodas; Jacob S. Hunter; Stephen J. Young; Kristina Lerman (J. Comput. Soc. Sci., 2018)

Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. - Enoch Yeung; Soumya Kundu; Nathan O. Hodas (ACC, 2019)

Metric-Based Few-Shot Learning for Video Action Recognition. - Chris Careaga; Brian Hutchinson; Nathan Oken Hodas; Lawrence Phillips (CoRR, 2019)

Explanatory Masks for Neural Network Interpretability. - Lawrence Phillips; Garrett B. Goh; Nathan O. Hodas (CoRR, 2019)

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. - Henry Kvinge; Zachary New; Nico Courts; Jung H. Lee; Lauren A. Phillips; Courtney D. Corley; Aaron Tuor; Andrew Avila; Nathan O. Hodas (MetaDL@AAAI, 2021)

Prototypical Region Proposal Networks for Few-Shot Localization and Classification. - Elliott Skomski; Aaron Tuor; Andrew Avila; Lauren A. Phillips; Zachary New; Henry Kvinge; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. - 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 (CoRR, 2021)

Adaptive Transfer Learning: a simple but effective transfer learning. - 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 (CoRR, 2021)