438 citations · 508 across the 12 of their papers we have counts for
7 papers · 1 filter
Novel deep learning methods for track reconstruction
Steven Farrell, Paolo Calafiura, Mayur Mudigonda +11
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew…
Graph Neural Networks for IceCube Signal Classification
Nicholas Choma, Federico Monti, Lisa Gerhardt +7
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly develop…
Optimizing the Union of Intersections LASSO () and Vector Autoregressive () Algorithms for Improved Statistical Estimation at Scale
Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook +4
The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive. Union of Intersections (…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…
Alchemist: An Apache Spark <=> MPI Interface
Alex Gittens, Kai Rothauge, Shusen Wang +6
The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur signif…
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Alex Gittens, Kai Rothauge, Shusen Wang +6
Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations…