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Published in Pre-print, 2018
We try to explore the task of relational reasoning (compositionality, physical interractions) via the specific problem of question answering in the space of plots and figures using the FigureQA dataset. We build on the ideas of task specific architectures such as Relation Networks and task generic architectures like FiLM to improve the state of the art performance on the FigureQA dataset
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Published in EDBT 2022, 2022
We present a system for optimal resource allocation in big data systems that can predict performance impact for candidate resource allocations, for both new and past observed queries. We introduce the notion of a performance characteristic curve (PCC) as a parameterized representation that can compactly capture the relationship between resources and performance. To tackle the challenge of training data sparsity, we introduce a novel data augmentation technique to efficiently synthesize the entire PCC using a single run of the query. Lastly, we demonstrate the advantages of a constrained loss function coupled with GNNs, over traditional ML methods, for capturing the domain specific behavior through an extensive experimental evaluation over SCOPE big data workloads at Microsoft. Our results show that our fast and novel skyline simulation technique for data augmentation is sufficiently accurate, with a median percentage error in run time estimates of 9% on past jobs and our ML models can estimate the run time of jobs for different token counts well, before they have executed, with a median percentage error of 39% or less.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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