33 citations · 38 across the 5 of their papers we have counts for
5 papers
EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution
Daniel Bourgeois, Zhimin Ding, Dimitrije Jankov +6
We consider the problem of automatically decomposing operations over tensors or arrays so that they can be executed in parallel on multiple devices. We address two, closely-linked…
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Yuxin Tang, Zhimin Ding, Dimitrije Jankov +3
The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally.…
Tensor Relational Algebra for Machine Learning System Design
Binhang Yuan, Dimitrije Jankov, Jia Zou +3
We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system? Current machine learning systems typically pu…
Lachesis: Automatic Partitioning for UDF-Centric Analytics
Jia Zou, Amitabh Das, Pratik Barhate +4
Persistent partitioning is effective in avoiding expensive shuffling operations. However it remains a significant challenge to automate this process for Big Data analytics workload…
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
Dimitrije Jankov, Shangyu Luo, Binhang Yuan +4
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set…