366 citations · 367 across the 2 of their papers we have counts for
7 papers
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Lars Hertel, Julian Collado, Peter Sadowski +2
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations…
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Jordan Ott, Mike Pritchard, Natalie Best +3
Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software lib…
Giving Up Control: Neurons as Reinforcement Learning Agents
Jordan Ott
Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts. All the while claiming to pursue the creation of Intelligen…
Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts
Natalie Best, Jordan Ott, Erik Linstead
Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing mode…
Questions to Guide the Future of Artificial Intelligence Research
Jordan Ott
The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observatio…
Learning in the Machine: To Share or Not to Share?
Jordan Ott, Erik Linstead, Nicholas LaHaye +1
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible.…