Search Results for author: Stratos Idreos

Found 6 papers, 2 papers with code

Proteus: A Self-Designing Range Filter

2 code implementations30 Jun 2022 Eric R. Knorr, Baptiste Lemaire, Andrew Lim, Siqiang Luo, Huanchen Zhang, Stratos Idreos, Michael Mitzenmacher

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.

More or Less: When and How to Build Neural Network Ensembles

no code implementations ICLR 2021 Abdul Wasay, Stratos Idreos

We identify a critical part of this design space that is not well-understood: That is how to decide between the alternatives of expanding a single network model or increasing the number of networks and using them together in an ensemble.


Learning Key-Value Store Design

no code implementations11 Jul 2019 Stratos Idreos, Niv Dayan, Wilson Qin, Mali Akmanalp, Sophie Hilgard, Andrew Ross, James Lennon, Varun Jain, Harshita Gupta, David Li, Zichen Zhu

The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs.

Layout Design

MotherNets: Rapid Deep Ensemble Learning

no code implementations12 Sep 2018 Abdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, Stratos Idreos

We propose MotherNets to enable higher accuracy and practical training cost for large and diverse neural network ensembles: A MotherNet captures the structural similarity across some or all members of a deep neural network ensemble which allows us to share data movement and computation costs across these networks.

Clustering Clustering Ensemble +2

Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores

1 code implementation1 Feb 2012 Felix Halim, Stratos Idreos, Panagiotis Karras, Roland H. C. Yap

Stochastic cracking also uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision-making.

Decision Making

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