331,869 research outputs found
DROP: Dimensionality Reduction Optimization for Time Series
Dimensionality reduction is a critical step in scaling machine learning
pipelines. Principal component analysis (PCA) is a standard tool for
dimensionality reduction, but performing PCA over a full dataset can be
prohibitively expensive. As a result, theoretical work has studied the
effectiveness of iterative, stochastic PCA methods that operate over data
samples. However, termination conditions for stochastic PCA either execute for
a predetermined number of iterations, or until convergence of the solution,
frequently sampling too many or too few datapoints for end-to-end runtime
improvements. We show how accounting for downstream analytics operations during
DR via PCA allows stochastic methods to efficiently terminate after operating
over small (e.g., 1%) subsamples of input data, reducing whole workload
runtime. Leveraging this, we propose DROP, a DR optimizer that enables speedups
of up to 5x over Singular-Value-Decomposition-based PCA techniques, and exceeds
conventional approaches like FFT and PAA by up to 16x in end-to-end workloads
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Objective: To compare different deep learning architectures for predicting
the risk of readmission within 30 days of discharge from the intensive care
unit (ICU). The interpretability of attention-based models is leveraged to
describe patients-at-risk. Methods: Several deep learning architectures making
use of attention mechanisms, recurrent layers, neural ordinary differential
equations (ODEs), and medical concept embeddings with time-aware attention were
trained using publicly available electronic medical record data (MIMIC-III)
associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was
used to compute the posterior over weights of an attention-based model. Odds
ratios associated with an increased risk of readmission were computed for
static variables. Diagnoses, procedures, medications, and vital signs were
ranked according to the associated risk of readmission. Results: A recurrent
neural network, with time dynamics of code embeddings computed by neural ODEs,
achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score:
0.372). Predictive accuracy was comparable across neural network architectures.
Groups of patients at risk included those suffering from infectious
complications, with chronic or progressive conditions, and for whom standard
medical care was not suitable. Conclusions: Attention-based networks may be
preferable to recurrent networks if an interpretable model is required, at only
marginal cost in predictive accuracy
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
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