
Anomaly Detection with Density Estimation
We leverage recent breakthroughs in neural density estimation to propose...
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Learning to grow: control of materials selfassembly using evolutionary reinforcement learning
We show that neural networks trained by evolutionary reinforcement learn...
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Simulation Assisted Likelihoodfree Anomaly Detection
Given the lack of evidence for new particle discoveries at the Large Had...
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Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale
The analysis of scientific data of increasing size and complexity requir...
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Towards Physicsinformed Deep Learning for Turbulent Flow Prediction
While deep learning has shown tremendous success in a wide range of doma...
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Surrogate Optimization of Deep Neural Networks for Groundwater Predictions
Sustainable management of groundwater resources under changing climatic ...
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Spatial sensitivity analysis for urban land use prediction with physicsconstrained conditional generative adversarial networks
Accurately forecasting urban development and its environmental and clima...
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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
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The use of Convolutional Neural Networks for signalbackground classification in Particle Physics experiments
The success of Convolutional Neural Networks (CNNs) in image classificat...
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Transfer Learning with Graph Neural Networks for ShortTerm Highway Traffic Forecasting
Highway traffic modeling and forecasting approaches are critical for int...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
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Performance Evaluation and Modeling of HPC I/O on NonVolatile Memory
HPC applications pose high demands on I/O performance and storage capabi...
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An Asynchronous Taskbased FanBoth Sparse Cholesky Solver
Systems of linear equations arise at the heart of many scientific and en...
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The Reverse CuthillMcKee Algorithm in DistributedMemory
Ordering vertices of a graph is key to minimize fillin and data structu...
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A Survey of High Level Frameworks in BlockStructured Adaptive Mesh Refinement Packages
Over the last decade blockstructured adaptive mesh refinement (SAMR) ha...
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Classification without labels: Learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful mod...
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Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising fro...
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Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically...
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CaloGAN: Simulating 3D High Energy Particle Showers in MultiLayer Electromagnetic Calorimeters with Generative Adversarial Networks
Simulation is a key component of physics analysis in particle physics an...
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Learning Particle Physics by Example: LocationAware Generative Adversarial Networks for Physics Synthesis
We provide a bridge between generative modeling in the Machine Learning ...
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Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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ExtremeWeather: A largescale climate dataset for semisupervised detection, localization, and understanding of extreme weather events
Then detection and identification of extreme weather events in largesca...
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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that...
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Dimension Reduction Using Rule Ensemble Machine Learning Methods: A Numerical Study of Three Ensemble Methods
Ensemble methods for supervised machine learning have become popular due...
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Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and LargeScale SSD Array Systems
Largescale systems with arrays of solid state disks (SSDs) have become ...
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TraceTracker: Hardware/Software CoEvaluation for LargeScale I/O Workload Reconstruction
Block traces are widely used for system studies, model verifications, an...
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Landau Collision Integral Solver with Adaptive Mesh Refinement on Emerging Architectures
The Landau collision integral is an accurate model for the smallangle d...
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Nanosurveyor: a framework for realtime data processing
Scientists are drawn to synchrotrons and accelerator based light sources...
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Deep Neural Networks for Physics Analysis on lowlevel wholedetector data at the LHC
There has been considerable recent activity applying deep convolutional ...
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BoxLib with Tiling: An AMR Software Framework
In this paper we introduce a blockstructured adaptive mesh refinement (...
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A LeftLooking Selected Inversion Algorithm and Task Parallelism on Shared Memory Systems
Given a sparse matrix A, the selected inversion algorithm is an efficien...
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A distributedmemory approximation algorithm for maximum weight perfect bipartite matching
We design and implement an efficient parallel approximation algorithm fo...
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Learning to Classify from Impure Samples
A persistent challenge in practical classification tasks is that labelle...
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A Spatial Mapping Algorithm with Applications in Deep LearningBased Structure Classification
Convolutional Neural Network (CNN)based machine learning systems have m...
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Distributed Caching for Complex Querying of Raw Arrays
As applications continue to generate multidimensional data at exponenti...
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Deep learning as a tool for neural data analysis: speech classification and crossfrequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure ...
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A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
We present memoryefficient and scalable algorithms for kernel methods u...
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Highperformance sparse matrixmatrix products on Intel KNL and multicore architectures
Sparse matrixmatrix multiplication (SpGEMM) is a computational primitiv...
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A 3D Parallel Algorithm for QR Decomposition
Interprocessor communication often dominates the runtime of large matrix...
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Spiking Linear Dynamical Systems on Neuromorphic Hardware for LowPower BrainMachine Interfaces
Neuromorphic architectures achieve lowpower operation by using many sim...
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Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering
The automatic reconstruction of threedimensional particle tracks from A...
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A probabilistic gridded product for daily precipitation extremes over the United States
Gridded data products, for example interpolated daily measurements of pr...
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Simple coarse graining and sampling strategies for image recognition
A conceptually simple way to recognize images is to directly compare tes...
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Adaptive Gaussian process surrogates for Bayesian inference
We present an adaptive approach to the construction of Gaussian process ...
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Exascale Deep Learning for Climate Analytics
We extract pixellevel masks of extreme weather patterns using variants ...
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Matrixfree construction of HSS representation using adaptive randomized sampling
We present new algorithms for the randomized construction of hierarchica...
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An Empirical Survey on Cosimulation: Promising Standards, Challenges and Research Needs
Cosimulation is a promising approach for the modelling and simulation o...
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Improving strong scaling of the Conjugate Gradient method for solving large linear systems using global reduction pipelining
This paper presents performance results comparing MPIbased implementati...
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Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Linear dimensionality reduction methods are commonly used to extract low...
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Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Interrogating Learned Representations
Interpretable representations of data are useful for testing a hypothesi...
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Berkeley Lab
Lawrence Berkeley National Laboratory, commonly referred to as Berkeley Lab, is a United States national laboratory that conducts scientific research on behalf of the United States Department of Energy.