Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification

no code implementations • EMNLP 2020 • Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy

Our user studies confirm that the learned LEs are explainable and capture domain semantics.

General Classification Inductive logic programming +4

Deep Learning with Apache SystemML

no code implementations • 8 Feb 2018 • Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen

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 shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning.

BIG-bench Machine Learning

Declarative Machine Learning - A Classification of Basic Properties and Types

no code implementations • 19 May 2016 • Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald

Specification alternatives range from ML algorithms expressed in domain-specific languages (DSLs) with optimization for performance, to ML task (learning problem) specifications with optimization for performance and accuracy.

BIG-bench Machine Learning Classification +1

Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs

no code implementations • 22 Mar 2015 • Matthias Boehm

We share our lessons learned in order to provide foundations for the optimization of ML programs.

BIG-bench Machine Learning