Nathan O. Hodas

31 papers A 1C 2Journal 23Unranked 5

Year	Rank	Type	Title / Venue / Authors
2021	J	`jnl`	Adaptive Transfer Learning: a simple but effective transfer learning. CoRR 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
2021	—	`conf`	Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. MetaDL@AAAI Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas
2021	J	`jnl`	One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. CoRR 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
2021	J	`jnl`	Prototypical Region Proposal Networks for Few-Shot Localization and Classification. CoRR Elliott Skomski, Aaron Tuor, Andrew Avila, Lauren A. Phillips, Zachary New, Henry Kvinge, Courtney D. Corley, Nathan O. Hodas
2020	J	`jnl`	Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. CoRR Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas
2019	J	`jnl`	Explanatory Masks for Neural Network Interpretability. CoRR Lawrence Phillips, Garrett B. Goh, Nathan O. Hodas
2019	C	`conf`	Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. ACC Enoch Yeung, Soumya Kundu, Nathan O. Hodas
2018	C	`conf`	A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. ACC Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas
2018	J	`jnl`	Doing the impossible: Why neural networks can be trained at all. CoRR Nathan O. Hodas, Panos Stinis
2018	J	`jnl`	Few-Shot Learning with Metric-Agnostic Conditional Embeddings. CoRR Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, Nathan O. Hodas
2018	A	`conf`	How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? WACV Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan A. Baker
2018	J	`jnl`	Model of Cognitive Dynamics Predicts Performance on Standardized Tests. CoRR Nathan O. Hodas, Jacob S. Hunter, Stephen J. Young, Kristina Lerman
2018	J	`jnl`	Model of cognitive dynamics predicts performance on standardized tests. J. Comput. Soc. Sci. Nathan O. Hodas, Jacob S. Hunter, Stephen J. Young, Kristina Lerman
2018	—	`conf`	SHARKZOR: Human in the Loop ML for User-Defined Image Classification. IUI Companion Meg Pirrung, Nathan Hilliard, Nancy O'Brien, Artëm Yankov, Court D. Corley, Nathan O. Hodas
2018	J	`jnl`	Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary. CoRR Meg Pirrung, Nathan Hilliard, Artëm Yankov, Nancy O'Brien, Paul Weidert, Courtney D. Corley, Nathan O. Hodas
2018	J	`jnl`	The Outer Product Structure of Neural Network Derivatives. CoRR Craig Bakker, Michael J. Henry, Nathan O. Hodas
2017	J	`jnl`	A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. CoRR Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas
2017	J	`jnl`	Bounded Rationality in Scholarly Knowledge Discovery. CoRR Kristina Lerman, Nathan O. Hodas, Hao Wu
2017	J	`jnl`	ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction. CoRR Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas
2017	J	`jnl`	Deep Learning for Computational Chemistry. CoRR Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
2017	J	`jnl`	Deep learning for computational chemistry. J. Comput. Chem. Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
2017	J	`jnl`	Dynamic Input Structure and Network Assembly for Few-Shot Learning. CoRR Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley
2017	J	`jnl`	How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? CoRR Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan A. Baker
2017	J	`jnl`	Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. CoRR Enoch Yeung, Soumya Kundu, Nathan O. Hodas
2017	J	`jnl`	SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. CoRR Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu
2017	—	`conf`	ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. CHI Extended Abstracts Fred Hohman, Nathan O. Hodas, Duen Horng Chau
2016	—	`conf`	How a User's Personality Influences Content Engagement in Social Media. SocInfo (1) Nathan O. Hodas, Ryan Butner, Court D. Corley
2016	J	`jnl`	How a user's personality influences content engagement in social media. CoRR Nathan O. Hodas, Ryan Butner, Courtney D. Corley
2016	J	`jnl`	Mutual information for fitting deep nonlinear models. CoRR Jacob S. Hunter, Nathan O. Hodas
2015	—	`conf`	Disentangling the Lexicons of Disaster Response in Twitter. WWW (Companion Volume) Nathan O. Hodas, Greg Ver Steeg, Joshua J. Harrison, Satish Chikkagoudar, Eric Bell, Courtney D. Corley
2010	J	`jnl`	The Quality of Oscillations in Overdamped Networks. CoRR Nathan O. Hodas