3 citations · 3 across the 4 of their papers we have counts for
9 papers
Machine Learning over Static and Dynamic Relational Data
Ahmet Kara, Milos Nikolic, Dan Olteanu +1
This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relatio…
Scalable Querying of Nested Data
Jaclyn Smith, Michael Benedikt, Milos Nikolic +1
While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested…
F-IVM: Learning over Fast-Evolving Relational Data
Milos Nikolic, Haozhe Zhang, Ahmet Kara +1
F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstra…
Maintaining Triangle Queries under Updates
Ahmet Kara, Milos Nikolic, Hung Q. Ngo +2
We consider the problem of incrementally maintaining the triangle queries with arbitrary free variables under single-tuple updates to the input relations. We introduce an approach…
Laconic Deep Learning Computing
Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz +2
We motivate a method for transparently identifying ineffectual computations in unmodified Deep Learning models and without affecting accuracy. Specifically, we show that if we deco…
DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing
Alberto Delmas, Sayeh Sharify, Patrick Judd +3
We show that selecting a single data type (precision) for all values in Deep Neural Networks, even if that data type is different per layer, amounts to worst case design. Much shor…