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BigDataGrapes D4.3 - Models and Tools for Predictive Analytics over Extremely Large Datasets
2018
Zenodo
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Layers of the BDG architecture contributed in Deliverable 4.1 "Methods and Tools for Scalable Distributed Processing". ...
The BDG software stack employs efficient and fault-tolerant tools for distributed processing, aimed at providing scalability and reliability for the target applications. ...
Layers of the BDG architecture contributed in Deliverable 4.1 "Methods and Tools for Scalable Distributed Processing"
D4.3 | Models and Tools for Predictive Analytics over Extremely Large Datasets 6 ...
doi:10.5281/zenodo.1481800
fatcat:rlqwgvajzre6pfxuiiclmk2r34
A Hadoop-Based Method to Predict Potential Effective Drug Combination
2014
BioMed Research International
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However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. ...
The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. ...
Extension to a Scalable Mining Process. ...
doi:10.1155/2014/196858
pmid:25147789
pmcid:PMC4134802
fatcat:ng2aqwb37jg43bw5jsykx5tfja
Predicting optimal operation of MC-3DSBC multidimensional scalable video coding using subjective quality measurement
2004
Visual Communications and Image Processing 2004
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For this purpose we firstly explore the behavior of SNR-Spatial-Temporal scalability using Motion Compensated (MC) SBC systems. ...
Based on the system behavior, we propose an efficient method for the optimal selection of scalability operator through contentbased prediction. ...
For the online processing routing, the content features of the incoming video will be used by the classification function. ...
doi:10.1117/12.527413
dblp:conf/vcip/WangNSC04
fatcat:ikuxigtxsjaq7kxdjiqttxivpm
BigDataGrapes D4.3 - Models and Tools for Predictive Analytics over Extremely Large Datasets
2019
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Layers of the BDG architecture contributed in Deliverable 4.1: "Methods and Tools for Scalable Distributed Processing". ...
The BDG software stack employs efficient and fault-tolerant tools for distributed processing, aimed at providing scalability and reliability for the target applications. ...
Layers of the BDG architecture contributed in Deliverable 4.1: "Methods and Tools for Scalable Distributed Processing". ...
doi:10.5281/zenodo.2641952
fatcat:n6ag6qt4gzg6tmnytqs2f7op4u
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
[article]
2018
arXiv
pre-print
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We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. ...
Our illustrative applications are based on challenging extreme multi-label classification problems with the extra burden of the very large number of class labels. ...
We call the resulting implementation strategy as fully scalable Gaussian processes because we achieve scalability in both N and D. ...
arXiv:1807.02537v2
fatcat:vd35plevurb4nccraxk546riim
Extreme Outcomes, Connectivity, and Power Laws: Towards an Econophysics of Organization
[chapter]
2013
Knowledge, Organization, and Management
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Organization science needs to find ways of studying extremes more rigorously. ...
Managers are often required to respond in adaptive ways to the threats and opportunities presented by rare, extreme outcomes. ...
How does "Paretian" scalable abduction differ from "Gaussian" abduction? By exploiting the scale-free character of extreme outcomes. ...
doi:10.1093/acprof:oso/9780199669165.003.0004
fatcat:wckr2yulfvagbij4kgsmq4nq4m
Scalable Variational Gaussian Process Classification
[article]
2014
arXiv
pre-print
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Gaussian process classification is a popular method with a number of appealing properties. ...
Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments. ...
arXiv:1411.2005v1 [stat.ML] 7 Nov 2014Gaussian Process Classification Gaussian process priors provide rich nonparametric models of functions. ...
arXiv:1411.2005v1
fatcat:iledvquffndcbh6sdbun7uo2wi
Deep Kernel Learning
[article]
2015
arXiv
pre-print
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We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process. ...
On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone ...
processes or other kernel machines for scalable learning. dimensions. ...
arXiv:1511.02222v1
fatcat:guzfr767yfaupjvorzo2rycmzy
Supervised Learning for Optimal Power Flow as a Real-Time Proxy
[article]
2016
arXiv
pre-print
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The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. ...
Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization ...
We show that for the test-cases inspected, extremely high accuracy for both feasibility classification and cost regression are obtainable. ...
arXiv:1612.06623v1
fatcat:pdinrey35rdgxmxws7ile42aku
Distributed Learning over Massive XML Documents in ELM Feature Space
2015
Mathematical Problems in Engineering
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Learning Machine for mining tasks of classification or clustering. ...
Extensive experiments are conducted on massive XML documents datasets to verify the effectiveness and efficiency for both classification and clustering applications. ...
H; (4) Calculate = H † T; Algorithm 3: Extreme Learning Machine for classification. ...
doi:10.1155/2015/923097
fatcat:2qxlvlx2pfesbnsgxzsgaplaea
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
[article]
2020
arXiv
pre-print
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This is carried out by modeling the AFs as adaptive Gaussian Processes (GP), which sit within a novel scalable NN topology, based on the Kolmogorov-Arnold Superposition Theorem (KST). ...
To demonstrate this, we perform a case study on a binary classification dataset of banknote authentication. ...
Gaussian Process activation functions, we are able to demonstrate a new degree of interpretability. ...
arXiv:2006.06057v1
fatcat:s5skggzrajbhhiypgluwxzccee
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
[article]
2016
arXiv
pre-print
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We present a general framework for classification of sparse and irregularly-sampled time series. ...
To address these challenges, we propose an uncertainty-aware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled ...
Gaussian processes for sparse and irregularly-sampled time series Our focus in this paper is on time series classification in the presence of sparse and irregular sampling. ...
arXiv:1606.04443v2
fatcat:3rnane45zjg4hlfufitmomkb2y
Large scale multi-label learning using Gaussian processes
2021
Machine Learning
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AbstractWe introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. ...
To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization ...
The whole process becomes somehow similar to negative sampling used in large scale classification and for learning word embeddings Mikolov et al. (2013) . ...
doi:10.1007/s10994-021-05952-5
fatcat:gd6fzrwagjf35b43wdl4qyrqgq
Generic Inference in Latent Gaussian Process Models
[article]
2018
arXiv
pre-print
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We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. ...
On the large-scale experiments involving prediction of airline delays and classification of handwritten digits, we show that our method is on par with the state-of-the-art hard-coded approaches for scalable ...
Conclusions and Discussion We have developed scalable automated variational inference for Gaussian process models (savigp), an inference method for models with Gaussian process (gp) priors, multiple outputs ...
arXiv:1609.00577v2
fatcat:ngpjezdvv5egheozyzc2pr3qya
Scalable neural architectures for end-to-end environmental sound classification
2022
Zenodo
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In particular, our architectures achieve state-of-the-art performance on UrbanSound8K in spectrogram classification (around 77%) with extreme compression factors (99.8%) with respect to current state-of-the-art ...
Here, urban sounds can be detected and processed by embedded devices in an Internet of Things (IoT) to identify meaningful events for municipalities or law enforcement. ...
Such scalability features allow for extreme model compression and optimization, while decoupling parameter count and computational cost in alignment with the harware-aware scaling paradigm. ...
doi:10.5281/zenodo.6351853
fatcat:bolkyk54pjbc3b3ikyhwte5wyq
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