Carianne Martinez
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View article: Flow-based parameterization for DAG and feature discovery in scientific multimodal data
Flow-based parameterization for DAG and feature discovery in scientific multimodal data Open
Representation learning algorithms are often used to extract essential features from high-dimensional datasets. These algorithms commonly assume that such features are independent. However, multimodal datasets containing complementary info…
View article: Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation
Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation Open
Many climate processes are characterized using large systems of nonlinear differential equations; this, along with the immense amount of data required to parameterize complex interactions, means that Earth-System Model (ESM) simulations ma…
View article: BeyondFingerprinting: AI-guided discovery of robust materials & processes
BeyondFingerprinting: AI-guided discovery of robust materials & processes Open
BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject…
View article: Unsupervised physics-informed disentanglement of multimodal data
Unsupervised physics-informed disentanglement of multimodal data Open
View article: A new parametrization of directed acyclic graphs and causal Markov kernels for scientific feature discovery
A new parametrization of directed acyclic graphs and causal Markov kernels for scientific feature discovery Open
View article: Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation
Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation Open
View article: A new parametrization of DAGs and causal Markov kernels for scientific feature discovery
A new parametrization of DAGs and causal Markov kernels for scientific feature discovery Open
View article: Unsupervised physics-informed disentanglement of multimodal data
Unsupervised physics-informed disentanglement of multimodal data Open
Here, we introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal datasets. Individual modalities are embedded into a shared latent space and fused throu…
View article: Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks
Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks Open
View article: High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning Open
We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide…
View article: Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario
Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario Open
As the prospect of exceeding global temperature targets set forth in the Paris Agreement becomes more likely, methods of climate intervention are increasingly being explored. With this increased interest there is a need for an assessment p…
View article: Causal disentanglement of multimodal data
Causal disentanglement of multimodal data Open
Causal representation learning algorithms discover lower-dimensional representations of data that admit a decipherable interpretation of cause and effect; as achieving such interpretable representations is challenging, many causal learning…
View article: Test and Evaluation of Systems with Embedded Machine Learning Components
Test and Evaluation of Systems with Embedded Machine Learning Components Open
As Machine Learning (ML) continues to advance, it is being integrated into more systems. Often, the ML component represents a significant portion of the system that reduces the burden on the end user or significantly improves task performa…
View article: Deep sequential neural network models improve stratification of suicide attempt risk among US veterans
Deep sequential neural network models improve stratification of suicide attempt risk among US veterans Open
Objective To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimat…
View article: Predicting Failure Using Deep Learning SAND Report
Predicting Failure Using Deep Learning SAND Report Open
Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burden…
View article: Causal disentanglement of multimodal data
Causal disentanglement of multimodal data Open
View article: Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation
Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation Open
Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volu…
View article: Dynamic x-ray diffraction of materials under ramp compression on the Thor pulsed-power generator
Dynamic x-ray diffraction of materials under ramp compression on the Thor pulsed-power generator Open
View article: Tools for Assessing Machine Learning Models' Performance in Real-World Settings
Tools for Assessing Machine Learning Models' Performance in Real-World Settings Open
View article: PACI: Performance Assessment for Climate Intervention
PACI: Performance Assessment for Climate Intervention Open
View article: Uncertainty-refined image segmentation under domain shift
Uncertainty-refined image segmentation under domain shift Open
A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out dur…
View article: Device and method for constructing and displaying high quality images from imaging data by transforming a data structure utilizing machine learning techniques
Device and method for constructing and displaying high quality images from imaging data by transforming a data structure utilizing machine learning techniques Open
Constructing a computer image from raw imaging data or encoded imaging data by transforming a first data structure in which the raw imaging data or the encoded imaging data is stored into a second data structure storing reorganized imaging…
View article: Credible, Automated Meshing of Images (CAMI).
Credible, Automated Meshing of Images (CAMI). Open
View article: Model Decision Tree.
Model Decision Tree. Open
View article: Barabsi Albert Algorithm.
Barabsi Albert Algorithm. Open
View article: Anomaly Detection in Images.
Anomaly Detection in Images. Open
View article: Likelihood Ratios for Out of Distribution Detection.
Likelihood Ratios for Out of Distribution Detection. Open
View article: Classification of Optical Ports Using Machine Learning.
Classification of Optical Ports Using Machine Learning. Open
View article: Phase to pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning.
Phase to pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning. Open
View article: Metric Decision Tree.
Metric Decision Tree. Open