18 citations · 40 across the 5 of their papers we have counts for
5 papers
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle
Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard +10
Machine learning (ML) applications become increasingly common in many domains. ML systems to execute these workloads include numerical computing frameworks and libraries, ML algori…
Deep Learning with Apache SystemML
Niketan Pansare, Michael Dusenberry, Nakul Jindal +3
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on s…
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML
Matthias Boehm, Berthold Reinwald, Dylan Hutchison +2
Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by means of linear algebra programs, and then automatically generate efficient execution plans.…
Declarative Machine Learning - A Classification of Basic Properties and Types
Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare +1
Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. T…
Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs
Matthias Boehm
Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging…