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Adaptive Segmentation Gaussian Mixtures Models for Approximating to Drastically Scaled-Various Sloped Long-Tail RTN Distributions
2013
International Journal of Future Computer and Communication
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The concepts central to the proposed method are 1) adaptive segmentation of the long-heavy tailed distributions such that the log-likelihood of GMM in each partition is maximized and 2) copy and paste ...
Index Terms-Mixtures of Gaussian, random telegraph noise, em algorithm, heavy-tail distribution, long-tail distribution, fail-bit analysis, static random access memory, guard band design. ...
This algorithm can allow approximating any heavy-long tailed distributions by the convenient short-tail Gaussian probability distributions. ...
doi:10.7763/ijfcc.2013.v2.195
fatcat:d34wd65klbdzjigaz34kct65di
A Look up Table Based Adaptive Segmentation Gaussian Mixtures Model for Fitting Complex Long-Tail RTN Distributions
2013
International Journal of Materials Mechanics and Manufacturing
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The concepts central to the proposed method are 1) LUT based fitting of all parameters of GMM and segmentation width and 2) adaptive segmentation of the long tailed distributions such that the log-likelihood ...
This paper proposes a look up table (LUT) based fitting method to approximate the mixtures of various sloped-Gamma tail distributions by an adaptive segmentation Gaussian mixtures model (GMM). ...
In order to solve the above issues, we propose, for the first time, a look up table (LUT) based fitting method to approximate any arbitrary long-tailed RTN distribution by an adaptive segmentation Gaussian ...
doi:10.7763/ijmmm.2013.v1.52
fatcat:jndljrksxnf33fkoftcikypi7e
Long-tailed Distribution Adaptation
[article]
2021
pre-print
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In this study, we formulate Long-tailed recognition as Domain Adaption (LDA), by modeling the long-tailed distribution as an unbalanced domain and the general distribution as a balanced domain. ...
on the long-tailed distribution to general distributions in an interpretable way. ...
To adapt the models trained upon long-tailed distributions to uniform distributions, re-sampling and re-weighting methods [3, 6] have been explored to re-balance the data distributions. ...
doi:10.1145/3474085.3475479
arXiv:2110.02686v1
fatcat:cw6g2uq7r5ejhp7oz3rsutt6zu
Adaptive linear rank tests for eQTL studies
2012
Statistics in Medicine
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In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. ...
We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. ...
KW has a high power for distributions with medium tails, whereas the median test (MED) and the long tails test (LT) are more powerful for long tails. ...
doi:10.1002/sim.5593
pmid:22933317
pmcid:PMC3739449
fatcat:b4qy5tn65rgd3hzqn3otemgzt4
Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
[article]
2022
arXiv
pre-print
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Current approaches handle the long-tailed problem to transform the long-tailed dataset to uniform distribution by re-sampling or re-weighting strategies. ...
Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution. ...
INTRODUCTION Long-tailed distribution is a common phenomenon in the real world [1] . ...
arXiv:2204.09234v2
fatcat:tcrpljifofdkpaownkczva4blq
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
[article]
2023
arXiv
pre-print
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We first find that the predictions of user preferences are biased under long-tail distributions. ...
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. ...
In this work, we consider the long-tail distributions from the item side (i.e. the long-tail item distribution), which treats users as samples in the long-tail distributions. ...
arXiv:2210.14309v3
fatcat:nkbb3wpra5fwtmsw47cvxtf2i4
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
2023
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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We first find that the predictions of user preferences are biased under long-tail distributions. ...
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. ...
In this work, we consider the long-tail distributions from the item side (i.e. the long-tail item distribution), which treats users as samples in the long-tail distributions. ...
doi:10.1145/3580305.3599814
fatcat:utizmpiktbbivkr7la2imqtbuq
Alleviating the Effect of Data Imbalance on Adversarial Training
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2307.10205v1 fatcat:zvh2u37b7nglfdgs3jww7dw5si
In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. ...
To combat that, we theoretically analyze the lower bound of the robust risk to train a model on a long-tailed dataset to obtain the key challenges in addressing the aforementioned dilemmas. ...
Long-tailed Recognition Long-tailed learning means training a machine learning model on a dataset that follows a long-tailed distribution. ...
arXiv:2307.10205v2
fatcat:l6clttmrvva5zhteisvklgejbq
Page 1627 of Genetics Vol. 180, Issue 3
[page]
2008
Genetics
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In the case of adaptation, then, the distribution of effects among beneficial muta- tions should be nearly exponential so long as the (unknown) fitness distribution belongs to the Gumbel
' Corresponding ...
By altering the value of k, we can tune whether the right tail of a fitness distribution behaves like the tail of a distribution ...
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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First of all, we connect existing classbalanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. ...
We analyze this mismatch from a domain adaptation point of view. ...
In long-tailed visual recognition, the marginal class distribution P s (y) of the source domain is long-tailed, and yet the class distribution P t (y) of the target domain is more balanced, e.g., a uniform ...
doi:10.1109/cvpr42600.2020.00763
dblp:conf/cvpr/JamalB0WG20
fatcat:hn6mlvfn5ba7ppymbl3ik3jxyq
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
[article]
2020
arXiv
pre-print
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First of all, we connect existing class-balanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. ...
Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model ...
The training set of iNaturalist 2018 exhibits a long-tailed class distribution [1] . ...
arXiv:2003.10780v1
fatcat:sjme4lkdmrfiboblan4aqafkty
Page 440 of Proceedings of the American Philosophical Society Vol. 105, Issue 4
[page]
1961
Proceedings of the American Philosophical Society
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long-tail mice ly
short tails and
of the with long tails and feet
prairies and forests, nice
ing in forested regions
The characters of these two groups are asso ciated with the two aspects of survival ...
) i
colored forms of the continental interior,
short hind feet, and darker forms mountains and coasts,
long hind
This distribution corresponds to that of the short-tail
living on the open prairies and ...
An Adaptive Test for the Two-Sample Location Problem Based onU-Statistics
2008
Communications in statistics. Simulation and computation
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We construct an adaptive test where all statistics involved are suitably chosen U-statistics. It is shown the adaptive test proposed has good asymptotic and finite power properties. ...
, 1974, and Hogg et al. (1975) , Long-tail (LT, Handl (1986) and Büning (1994) , both for long tails) and Hogg-Fisher-Randles (HFR, for right-skew densities). ...
On the left side we have densities with long tails, in the centre that with medium tails, and on the right that with short tails. ...
doi:10.1080/03610910801983152
fatcat:ud3xyvkgi5cnfbunte5gfixfpu
Dual Channel with Involution for Long-Tailed Visual Recognition
2022
Open Journal of Applied Sciences
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With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. ...
The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed ...
long-tailed distribution. ...
doi:10.4236/ojapps.2022.124029
fatcat:llsq7vmhwjagzduicr34domg7e
Trimmed Mean As An Adaptive Robust Estimator Of A Location Parameter For Weibull Distribution
2008
Zenodo
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The outliers arise from gross errors or contamination from distributions with long tails. The trimmed mean is a robust estimate. ...
The findings of the study revealed that the tail lengths of the Weibull distribution increase in magnitudes as the trimming proportions increase, the measure of the tail length and the adaptive trimmed ...
with long tails. ...
doi:10.5281/zenodo.1074333
fatcat:dce4ldgi6jchtmhgfrc5q3udm4
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