5 citations · 7 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
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…