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Nathan O. Hodas

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Adaptive Transfer Learning: a simple but effective transfer learning

Nov 22, 2021
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

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One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

Jun 02, 2021
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

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Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Sep 23, 2020
Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas

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The Outer Product Structure of Neural Network Derivatives

Oct 09, 2018
Craig Bakker, Michael J. Henry, Nathan O. Hodas

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Doing the impossible: Why neural networks can be trained at all

May 28, 2018
Nathan O. Hodas, Panos Stinis

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How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

Mar 18, 2018
Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan Baker

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SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

Mar 18, 2018
Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu

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Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

Mar 18, 2018
Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas

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Few-Shot Learning with Metric-Agnostic Conditional Embeddings

Feb 12, 2018
Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, Nathan O. Hodas

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Dynamic Input Structure and Network Assembly for Few-Shot Learning

Aug 22, 2017
Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley

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