b'Prabhat'

DeepAI AI Chat

Featured Co-authors

Jian Zhang
218 publications
Anima Anandkumar
180 publications
Michael W. Mahoney
119 publications
Matthias Nießner
108 publications
Joan Bruna
93 publications
Frank Wood
79 publications
Liu Yang
70 publications
Christopher Pal
59 publications
Michael M. Bronstein
59 publications
Jialin Liu
56 publications
Ryan P. Adams
56 publications

research

∙ 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 ...

26 Luca Pion-Tonachini, et al. ∙

research

∙ 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 ...

0 Nicholas Choma, et al. ∙

research

∙ 05/01/2020

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...

3 Chiyu "Max" Jiang, et al. ∙

research

∙ 10/29/2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central...

42 Liu Yang, et al. ∙

research

∙ 09/25/2019

DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Extracting actionable insight from complex unlabeled scientific data is ...

0 Adam Rupe, et al. ∙

research

∙ 09/16/2019

Towards Unsupervised Segmentation of Extreme Weather Events

Extreme weather is one of the main mechanisms through which climate chan...

0 Adam Rupe, et al. ∙

research

∙ 07/08/2019

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...

2 Atılım Güneş Baydin, et al. ∙

research

∙ 05/13/2019

Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

Simulating complex physical systems often involves solving partial diffe...

6 Jin-Long Wu, et al. ∙

research

∙ 01/07/2019

Spherical CNNs on Unstructured Grids

We present an efficient convolution kernel for Convolutional Neural Netw...

14 Chiyu "Max" Jiang, et al. ∙

research

∙ 10/03/2018

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants ...

0 Thorsten Kurth, et al. ∙

research

∙ 09/17/2018

Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph- and manifold-structure...

2 Nicholas Choma, et al. ∙

research

∙ 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...

8 Mahesh Balasubramanian, et al. ∙

research

∙ 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...

0 Amrita Mathuriya, et al. ∙

research

∙ 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...

4 Atilim Gunes Baydin, et al. ∙

research

∙ 06/03/2018

Alchemist: An Apache Spark <=> MPI Interface

The Apache Spark framework for distributed computation is popular in the...

0 Alex Gittens, et al. ∙

research

∙ 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...

0 Alex Gittens, et al. ∙

research

∙ 02/28/2018

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical c...

0 Jeffrey Regier, et al. ∙

research

∙ 01/31/2018

Cataloging the Visible Universe through Bayesian Inference at Petascale

Astronomical catalogs derived from wide-field imaging surveys are an imp...

0 Jeffrey Regier, et al. ∙

research

∙ 12/26/2017

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

We explore scaling of the standard distributed Tensorflow with GRPC prim...

0 Amrita Mathuriya, et al. ∙

research

∙ 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...

0 Mario Lezcano Casado, et al. ∙

research

∙ 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 ...

0 Wahid Bhimji, et al. ∙

research

∙ 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...

0 Brian Friesen, et al. ∙

research

∙ 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...

0 Eli Dart, et al. ∙

research

∙ 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...

0 Thorsten Kurth, et al. ∙

research

∙ 05/22/2017

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

The increasing size and complexity of scientific data could dramatically...

0 Kristofer E. Bouchard, et al. ∙

research

∙ 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...

0 Evan Racah, et al. ∙

research

∙ 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 ...

0 Jeffrey Regier, et al. ∙

research

∙ 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...

0 Evan Racah, et al. ∙

research

∙ 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...

0 Jeffrey Regier, et al. ∙

research

∙ 02/19/2015

Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian optimization is an effective methodology for the global optimiz...

0 Jasper Snoek, et al. ∙