136 citations · 231 across the 11 of their papers we have counts for
6 papers · 1 filter
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Garrett B. Goh, Nathan O. Hodas, Charles Siegel +1
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is…
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representat…
Bounded Rationality in Scholarly Knowledge Discovery
Kristina Lerman, Nathan Hodas, Hao Wu
In an information-rich world, people's time and attention must be divided among rapidly changing information sources and the diverse tasks demanded of them. How people decide which…
A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems
Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas
In this paper, we consider the problem of quantifying controllability and observability of a nonlinear discrete time dynamical system. We introduce the Koopman operator as a canoni…
Dynamic Input Structure and Network Assembly for Few-Shot Learning
Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception. While methods have succeeded with large amounts of traini…
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
Enoch Yeung, Soumya Kundu, Nathan Hodas
The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by…