459 citations · 1.1k across the 17 of their papers we have counts for
6 papers · 1 filter
Deep-Learning-Based Kinematic Reconstruction for DUNE
Junze Liu, Jordan Ott, Julian Collado +4
In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of remain unknown. The Deep Underground Neutrino Expe…
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Griffin Mooers, Mike Pritchard, Tom Beucler +4
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Par…
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…