The Experts below are selected from a list of 15483 Experts worldwide ranked by ideXlab platform
Andy Baker - One of the best experts on this subject based on the ideXlab platform.
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Principal Filter analysis for luminescence excitation‐emission data
Geophysical Research Letters, 2002Co-Authors: Chris Brunsdon, Andy BakerAbstract:[1] A new method (termed Principal Filter Analysis (PFA)) for analysing large time series of luminescence excitation-emission matrices (EEMs) is proposed, based on the idea of identifying ‘Filters’ that detect time periods where interesting variations in the EEMs occur. A mathematical exposition of the technique is supplied, followed by a discusion of how it may be implemented in practice. The method is applied to EEMs taken from a stalagmite in Crag Cave, W. Ireland resulting in three distinct time periods of luminescence properties being identified.
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Principal Filter analysis for luminescence excitation emission data
Geophysical Research Letters, 2002Co-Authors: Chris Brunsdon, Andy BakerAbstract:[1] A new method (termed Principal Filter Analysis (PFA)) for analysing large time series of luminescence excitation-emission matrices (EEMs) is proposed, based on the idea of identifying ‘Filters’ that detect time periods where interesting variations in the EEMs occur. A mathematical exposition of the technique is supplied, followed by a discusion of how it may be implemented in practice. The method is applied to EEMs taken from a stalagmite in Crag Cave, W. Ireland resulting in three distinct time periods of luminescence properties being identified.
Chris Brunsdon - One of the best experts on this subject based on the ideXlab platform.
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Principal Filter analysis for luminescence excitation‐emission data
Geophysical Research Letters, 2002Co-Authors: Chris Brunsdon, Andy BakerAbstract:[1] A new method (termed Principal Filter Analysis (PFA)) for analysing large time series of luminescence excitation-emission matrices (EEMs) is proposed, based on the idea of identifying ‘Filters’ that detect time periods where interesting variations in the EEMs occur. A mathematical exposition of the technique is supplied, followed by a discusion of how it may be implemented in practice. The method is applied to EEMs taken from a stalagmite in Crag Cave, W. Ireland resulting in three distinct time periods of luminescence properties being identified.
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Principal Filter analysis for luminescence excitation emission data
Geophysical Research Letters, 2002Co-Authors: Chris Brunsdon, Andy BakerAbstract:[1] A new method (termed Principal Filter Analysis (PFA)) for analysing large time series of luminescence excitation-emission matrices (EEMs) is proposed, based on the idea of identifying ‘Filters’ that detect time periods where interesting variations in the EEMs occur. A mathematical exposition of the technique is supplied, followed by a discusion of how it may be implemented in practice. The method is applied to EEMs taken from a stalagmite in Crag Cave, W. Ireland resulting in three distinct time periods of luminescence properties being identified.
Nicholas Apostoloff - One of the best experts on this subject based on the ideXlab platform.
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WACV - Filter Distillation for Network Compression
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020Co-Authors: Xavier Suau, Uca Zappella, Nicholas ApostoloffAbstract:In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between Filter responses within network layers to recommend a smaller network that maintain as much as possible the accuracy of the full model. We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses. Since PFA compresses networks based on the correlation of their responses we show in our experiments that it gains the additional flexibility of adapting each architecture to a specific domain while compressing. PFA is evaluated against several architectures and datasets, and shows considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. Our tests show that PFA is competitive with state-of-the-art approaches while removing adoption barriers thanks to its practical implementation, intuitive philosophy and ease of use.
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NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Xavier Suau, Luca Zappella, Nicholas ApostoloffAbstract:Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between Filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a heuristic that leads to a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio. Finally, we show how PFA is an effective tool for simultaneous compression and domain adaptation.
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Filter Distillation for Network Compression
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Xavier Suau, Luca Zappella, Nicholas ApostoloffAbstract:In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between Filter responses within network layers to recommend a smaller network that maintain as much as possible the accuracy of the full model. We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses. Since PFA compresses networks based on the correlation of their responses we show in our experiments that it gains the additional flexibility of adapting each architecture to a specific domain while compressing. PFA is evaluated against several architectures and datasets, and shows considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. Our tests show that PFA is competitive with state-of-the-art approaches while removing adoption barriers thanks to its practical implementation, intuitive philosophy and ease of use.
Niovi Kehayopulu - One of the best experts on this subject based on the ideXlab platform.
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Principal Filters of some ordered $\Gamma$-semigroups
Republic of Armenia National Academy of Sciences, 2016Co-Authors: Niovi Kehayopulu, Michael TsingelisAbstract:For an intra-regular or a left regular and left duo ordered $\Gamma$-semigroup $M$, we describe the Principal Filter of $M$ which plays an essential role in the structure of this type of $po$-$\Gamma$-semigroups. We also prove that an ordered $\Gamma$-semigroup $M$ is intra-regular if and only if the ideals of $M$ are semiprime and it is left (right) regular and left (right) duo if and only if the left (right) ideals of $M$ are semiprime.
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Decomposition of intra-regular $po$-$\Gamma$-semigroups into simple components
arXiv: General Mathematics, 2015Co-Authors: Niovi KehayopuluAbstract:We keep the definition of intra-regularity (left regularity) of $po$-$\Gamma$-semigroups introduced in arXiv: 1511.00679 which is absolutely necessary for the investigation. Being able to describe the form of the elements of the Principal Filter by using this definition, we study the decomposition of an intra-regular $po$-$\Gamma$-semigroup into simple components. Then we prove that a $po$-$\Gamma$-semigroup $M$ is intra-regular and the ideals of $M$ form a chain if and only if $M$ is a chain of simple semigroups. Moreover, a $po$-$\Gamma$-semigroup $M$ is intra-regular and the ideals of $M$ form a chain if and only if the ideals of $M$ are prime. Finally, for an intra-regular $po$-$\Gamma$-semigroup $M$, the set $\{(x)_{\cal N} \mid x\in M\}$ coincides with the set of all maximal simple subsemigroups of $M$. A decomposition of left regular and left duo $po$-$\Gamma$-semigroup into left simple components has been also given.
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On intra-regular and left regular and left duo ordered $\Gamma$-semigroups
arXiv: Rings and Algebras, 2015Co-Authors: Niovi Kehayopulu, Michael TsingelisAbstract:For an intra-regular or a left regular and left duo ordered $\Gamma$-semigroup $M$, we describe the Principal Filter of $M$ which plays an essential role in the structure of this type of $po$-$\Gamma$-semigroups. We also prove that an ordered $\Gamma$-semigroup $M$ is intra-regular if and only if the ideals of $M$ are semiprime and it is left (right) regular and left (right) duo if and only if the left (right) ideals of $M$ are semiprime.
Michael Tsingelis - One of the best experts on this subject based on the ideXlab platform.
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Principal Filters of some ordered $\Gamma$-semigroups
Republic of Armenia National Academy of Sciences, 2016Co-Authors: Niovi Kehayopulu, Michael TsingelisAbstract:For an intra-regular or a left regular and left duo ordered $\Gamma$-semigroup $M$, we describe the Principal Filter of $M$ which plays an essential role in the structure of this type of $po$-$\Gamma$-semigroups. We also prove that an ordered $\Gamma$-semigroup $M$ is intra-regular if and only if the ideals of $M$ are semiprime and it is left (right) regular and left (right) duo if and only if the left (right) ideals of $M$ are semiprime.
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On intra-regular and left regular and left duo ordered $\Gamma$-semigroups
arXiv: Rings and Algebras, 2015Co-Authors: Niovi Kehayopulu, Michael TsingelisAbstract:For an intra-regular or a left regular and left duo ordered $\Gamma$-semigroup $M$, we describe the Principal Filter of $M$ which plays an essential role in the structure of this type of $po$-$\Gamma$-semigroups. We also prove that an ordered $\Gamma$-semigroup $M$ is intra-regular if and only if the ideals of $M$ are semiprime and it is left (right) regular and left (right) duo if and only if the left (right) ideals of $M$ are semiprime.