Search Results for author: Jordan Ott

Found 9 papers, 5 papers with code

Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks

no code implementations30 Jan 2021 Jordan Ott, David Bruyette, Cody Arbuckle, Dylan Balsz, Silke Hecht, Lisa Shubitz, Pierre Baldi

We also use the classification model to identify regions of interest and localize the disease in the radiographic images, as illustrated through visual heatmaps.

Deep-Learning-Based Kinematic Reconstruction for DUNE

no code implementations11 Dec 2020 Junze Liu, Jordan Ott, Julian Collado, Benjamin Jargowsky, Wenjie Wu, Jianming Bian, Pierre Baldi

To precisely reconstruct these kinematic characteristics of detected interactions at DUNE, we have developed and will present two CNN-based methods, 2-D and 3-D, for the reconstruction of final state particle direction and energy, as well as neutrino energy.

A Fortran-Keras Deep Learning Bridge for Scientific Computing

2 code implementations14 Apr 2020 Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras.

Giving Up Control: Neurons as Reinforcement Learning Agents

1 code implementation17 Mar 2020 Jordan Ott

Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts.

reinforcement-learning

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

no code implementations3 Mar 2020 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 models previously trained for another task.

Transfer Learning

Questions to Guide the Future of Artificial Intelligence Research

no code implementations21 Dec 2019 Jordan Ott

Biology has clear constraints but by not using it as a guide we are constraining ourselves.

Learning in the Machine: To Share or Not to Share?

1 code implementation23 Sep 2019 Jordan Ott, Erik Linstead, Nicholas LaHaye, Pierre Baldi

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes.

Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems

3 code implementations3 Sep 2019 Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, Pierre Gentine

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.

Computational Physics Atmospheric and Oceanic Physics

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