Search Results for author: Daniel C. Elton

Found 14 papers, 4 papers with code

Induction, Popper, and machine learning

no code implementations2 Oct 2021 Bruce Nielson, Daniel C. Elton

Moreover, at a more meta level the process of development of all AI algorithms can be understood under the framework of universal Darwinism.

Applying Deutsch's concept of good explanations to artificial intelligence and neuroscience -- an initial exploration

no code implementations16 Dec 2020 Daniel C. Elton

We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.

Deep Small Bowel Segmentation with Cylindrical Topological Constraints

no code implementations16 Jul 2020 Seung Yeon Shin, Sung-Won Lee, Daniel C. Elton, James L. Gulley, Ronald M. Summers

Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.

Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model

no code implementations14 Jul 2020 Yingying Zhu, You-Bao Tang, Yu-Xing Tang, Daniel C. Elton, Sung-Won Lee, Perry J. Pickhardt, Ronald M. Summers

We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.

Image-to-Image Translation Pancreas Segmentation +1

Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection

no code implementations22 May 2020 Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers

In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.

Image Reconstruction Object Detection +1

Self-explaining AI as an alternative to interpretable AI

no code implementations12 Feb 2020 Daniel C. Elton

While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work.

Autonomous Vehicles

Accurately identifying vertebral levels in large datasets

no code implementations28 Jan 2020 Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt, Ronald M. Summers

We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.

Instance Segmentation Semantic Segmentation

Phonon Lifetimes and Thermal Conductivity of the Molecular Crystal $α$-RDX

1 code implementation26 Apr 2019 Gaurav Kumar, Francis G. VanGessel, Daniel C. Elton, Peter W. Chung

This is likely because diffusive carriers contribute to over 95% of the thermal conductivity in ${\alpha}$-RDX.

Materials Science

Deep learning for molecular design - a review of the state of the art

no code implementations11 Mar 2019 Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, Peter W. Chung

In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.

Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

no code implementations1 Mar 2019 Daniel C. Elton, Dhruv Turakhia, Nischal Reddy, Zois Boukouvalas, Mark D. Fuge, Ruth M. Doherty, Peter W. Chung

The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.

Word Embeddings

Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning

1 code implementation1 Nov 2018 Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge

Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.

Drug Discovery Feature Selection +1

A Phonon Boltzmann Study of Microscale Thermal Transport in $α$-RDX Cook-Off

1 code implementation24 Aug 2018 Francis G. VanGessel, Gaurav Kumar, Daniel C. Elton, Peter W. Chung

The microscale thermal transport properties of $\alpha$RDX are believed to be major factors in the initiation process.

Materials Science

Machine Learning of Energetic Material Properties

2 code implementations17 Jul 2018 Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas, DeCarlos E. Taylor, William D. Mattson, Mark D. Fuge, Peter W. Chung

In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.

Materials Science Chemical Physics Computational Physics

Using a monomer potential energy surface to perform approximate path integral molecular dynamics simulation of ab-initio water with near-zero added cost

no code implementations15 Mar 2018 Daniel C. Elton, Michelle Fritz, M. -V. Fernández-Serra

We show that our method, which we call "monomer PIMD", correctly captures changes in the structure of water found in a full PIMD simulation but at much lower computational cost.

Chemical Physics Soft Condensed Matter Computational Physics

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