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11/27/2021
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility ...
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06/30/2020
Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...
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05/01/2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...
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10/29/2019
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central...
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09/25/2019
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Extracting actionable insight from complex unlabeled scientific data is ...
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09/16/2019
Towards Unsupervised Segmentation of Extreme Weather Events
Extreme weather is one of the main mechanisms through which climate chan...
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07/08/2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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05/13/2019
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Simulating complex physical systems often involves solving partial diffe...
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01/07/2019
Spherical CNNs on Unstructured Grids
We present an efficient convolution kernel for Convolutional Neural Netw...
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10/03/2018
Exascale Deep Learning for Climate Analytics
We extract pixel-level masks of extreme weather patterns using variants ...
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09/17/2018
Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph- and manifold-structure...
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08/21/2018
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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08/14/2018
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that d...
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07/20/2018
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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06/03/2018
Alchemist: An Apache Spark <=> MPI Interface
The Apache Spark framework for distributed computation is popular in the...
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05/30/2018
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Apache Spark is a popular system aimed at the analysis of large data set...
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02/28/2018
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
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01/31/2018
Cataloging the Visible Universe through Bayesian Inference at Petascale
Astronomical catalogs derived from wide-field imaging surveys are an imp...
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12/26/2017
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
We explore scaling of the standard distributed Tensorflow with GRPC prim...
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12/21/2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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11/09/2017
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
There has been considerable recent activity applying deep convolutional ...
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08/31/2017
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies
The nature of dark energy and the complete theory of gravity are two cen...
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08/26/2017
An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6
We document the data transfer workflow, data transfer performance, and o...
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08/17/2017
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
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05/22/2017
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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12/07/2016
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Then detection and identification of extreme weather events in large-sca...
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11/10/2016
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Celeste is a procedure for inferring astronomical catalogs that attains ...
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01/28/2016
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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06/03/2015
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets...
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02/19/2015
