
Probabilistic partition of unity networks: clustering based deep approximation
Partition of unity networks (POUNets) have been shown capable of realiz...
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Ensemble Grammar Induction For Detecting Anomalies in Time Series
Time series anomaly detection is an important task, with applications in...
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PlasticityEnhanced DomainWall MTJ Neural Networks for EnergyEfficient Online Learning
Machine learning implements backpropagation via abundant training sample...
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On the Accuracy of Analog Neural Network Inference Accelerators
Specialized accelerators have recently garnered attention as a method to...
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Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Despite the potential of Machine learning (ML) to learn the behavior of ...
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Datadriven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenge...
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Contextmodulation of hippocampal dynamics and deep convolutional networks
Complex architectures of biological neural circuits, such as parallel pr...
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Probability Series Expansion Classifier that is Interpretable by Design
This work presents a new classifier that is specifically designed to be ...
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Allatonce Optimization for Coupled Matrix and Tensor Factorizations
Joint analysis of data from multiple sources has the potential to improv...
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COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
COMET is a singlepass MapReduce algorithm for learning on largescale d...
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A distributedmemory hierarchical solver for general sparse linear systems
We present a parallel hierarchical solver for general sparse linear syst...
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Conservative model reduction for finitevolume models
This work proposes a method for model reduction of finitevolume models ...
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Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host...
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Active Betweenness Cardinality: Algorithms and Applications
Centrality rankings such as degree, closeness, betweenness, Katz, PageRa...
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Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures
Quantifying simulation uncertainties is a critical component of rigorous...
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Spacetime leastsquares PetrovGalerkin projection for nonlinear model reduction
This work proposes a spacetime leastsquares PetrovGalerkin (STLSPG) ...
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Compressive Sensing with CrossValidation and StopSampling for Sparse Polynomial Chaos Expansions
Compressive sensing is a powerful technique for recovering sparse soluti...
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Provable and practical approximations for the degree distribution using sublinear graph samples
The degree distribution is one of the most fundamental properties used i...
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Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation...
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Multithreaded Sparse MatrixMatrix Multiplication for ManyCore and GPU Architectures
Sparse MatrixMatrix multiplication is a key kernel that has application...
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Embedded Model Error Representation for Bayesian Model Calibration
Model error estimation remains one of the key challenges in uncertainty ...
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Sparse MatrixMatrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments
Architectures with multiple classes of memory media are becoming a commo...
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Bayesian Updating and Uncertainty Quantification using Sequential Tempered MCMC with the RankOne Modified Metropolis Algorithm
Bayesian methods are critical for quantifying the behaviors of systems. ...
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Spiking Neural Algorithms for Markov Process Random Walk
The random walk is a fundamental stochastic process that underlies many ...
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A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data
In this paper, we develop a method for constructing tolerance bounds for...
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Explicating feature contribution using Random Forest proximity distances
In Random Forests, proximity distances are a metric representation of da...
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Asynchronous OneLevel and TwoLevel Domain Decomposition Solvers
Parallel implementations of linear iterative solvers generally alternate...
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Software for Sparse Tensor Decomposition on Emerging Computing Architectures
In this paper, we develop software for decomposing sparse tensors that i...
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Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
This paper presents a new technique for training networks for lowprecis...
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A Generalized Framework for Approximate Control Variates
We describe and analyze a Monte Carlo (MC) sampling framework for accele...
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Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained
Software is the key crosscutting technology that enables advances in mat...
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Recovering missing CFD data for highorder discretizations using deep neural networks and dynamics learning
Data I/O poses a significant bottleneck in largescale CFD simulations; ...
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Nearly all modelreduction techniques project the governing equations on...
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The Online EventDetection Problem
Given a stream S = (s_1, s_2, ..., s_N), a ϕheavy hitter is an item s_i...
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Statistical closure modeling for reducedorder models of stationary systems by the ROMES method
This work proposes a technique for constructing a statistical closure mo...
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Distillation Strategies for Proximal Policy Optimization
Visionbased deep reinforcement learning (RL), similar to deep learning,...
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Virtually the Same: Comparing Physical and Virtual Testbeds
Network designers, planners, and security professionals increasingly rel...
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Fractional Operators Applied to Geophysical Electromagnetics
A growing body of applied mathematics literature in recent years has foc...
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Online adaptive basis refinement and compression for reducedorder models
In many applications, projectionbased reducedorder models (ROMs) have ...
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Asymptotically compatible meshfree discretization of statebased peridynamics for linearly elastic composite materials
Statebased peridynamic models provide an important extension of bondba...
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A New Approach for Distributed Hypothesis Testing with Extensions to ByzantineResilience
We study a setting where a group of agents, each receiving partially inf...
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A Usercentered Design Study in Scientific Visualization Targeting Domain Experts
The development and design of visualization solutions that are truly usa...
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Coarse Quad Layouts Through Robust Simplification of Cross Field Separatrix Partitions
Streamlinebased quad meshing algorithms use smooth cross fields to part...
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Composing Neural Algorithms with Fugu
Neuromorphic hardware architectures represent a growing family of potent...
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Solving LargeScale 01 Knapsack Problems and its Application to Point Cloud Resampling
01 knapsack is of fundamental importance in computer science, business,...
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Efficient IMEX RungeKutta methods for nonhydrostatic dynamics
We analyze the stability and accuracy (up to third order) of a new famil...
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A Review of Machine Learning Applications in Fuzzing
Fuzzing has played an important role in improving software development a...
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Polynomial Preconditioned GMRES to Reduce Communication in Parallel Computing
Polynomial preconditioning with the GMRES minimal residual polynomial ha...
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A New Approach to Distributed Hypothesis Testing and NonBayesian Learning: Improved Learning Rate and ByzantineResilience
We study a setting where a group of agents, each receiving partially inf...
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Asymptotically compatible reproducing kernel collocation and meshfree integration for nonlocal diffusion
Reproducing kernel (RK) approximations are meshfree methods that constru...
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Sandia National Laboratories
The Sandia National Laboratories, managed and operated by the National Technology and Engineering Solutions of Sandia, is one of three National Nuclear Security Administration research and development laboratories.