Mark Tygert | DeepAI

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∙ 05/18/2023

Cumulative differences between paired samples

The simplest, most common paired samples consist of observations from tw...

0 Isabel Kloumann, et al. ∙

∙ 03/03/2023

An efficient algorithm for integer lattice reduction

A lattice of integers is the collection of all linear combinations of a ...

0 François Charton, et al. ∙

∙ 07/27/2022

Ties in ranking scores can be treated as weighted samples

Prior proposals for cumulative statistics suggest making tiny random per...

0 Mark Tygert, et al. ∙

∙ 05/19/2022

Metrics of calibration for probabilistic predictions

Predictions are often probabilities; e.g., a prediction could be for pre...

0 Imanol Arrieta Ibarra, et al. ∙

∙ 01/31/2022

Calibration of P-values for calibration and for deviation of a subpopulation from the full population

The author's recent research papers, "Cumulative deviation of a subpopul...

0 Mark Tygert, et al. ∙

∙ 12/01/2021

Controlling for multiple covariates

A fundamental problem in statistics is to compare the outcomes attained ...

0 Mark Tygert, et al. ∙

∙ 08/05/2021

Cumulative differences between subpopulations

Comparing the differences in outcomes (that is, in "dependent variables"...

0 Mark Tygert, et al. ∙

∙ 08/04/2020

Plotting the cumulative deviation of a subgroup from the full population as a function of score

Assessing whether a subgroup of a full population is getting treated equ...

0 Mark Tygert, et al. ∙

∙ 06/03/2020

An optimizable scalar objective value cannot be objective and should not be the sole objective

This paper concerns the ethics and morality of algorithms and computatio...

0 Isabel Kloumann, et al. ∙

∙ 06/03/2020

Plots of the cumulative differences between observed and expected values of ordered Bernoulli variates

Many predictions are probabilistic in nature; for example, a prediction ...

0 Mark Tygert, et al. ∙

∙ 01/09/2020

Secure multiparty computations in floating-point arithmetic

Secure multiparty computations enable the distribution of so-called shar...

0 Chuan Guo, et al. ∙

∙ 02/02/2019

Methods of interpreting error estimates for grayscale image reconstructions

One representation of possible errors in a grayscale image reconstructio...

0 Aaron Defazio, et al. ∙

∙ 11/19/2018

Simulating single-coil MRI from the responses of multiple coils

We convert the information-rich measurements of parallel and phased-arra...

0 Mark Tygert, et al. ∙

∙ 09/18/2018

Compressed sensing with a jackknife and a bootstrap

Compressed sensing proposes to reconstruct more degrees of freedom in a ...

0 Mark Tygert, et al. ∙

∙ 09/01/2017

Hierarchical loss for classification

Failing to distinguish between a sheepdog and a skyscraper should be wor...

0 Cinna Wu, et al. ∙

∙ 02/09/2016

Poor starting points in machine learning

Poor (even random) starting points for learning/training/optimization ar...

0 Mark Tygert, et al. ∙

∙ 06/26/2015

Convolutional networks and learning invariant to homogeneous multiplicative scalings

The conventional classification schemes -- notably multinomial logistic ...

0 Mark Tygert, et al. ∙

∙ 03/11/2015

A mathematical motivation for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated...

0 Joan Bruna, et al. ∙

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