This work presents a mechanism to debias polynomial functions computed from locally differentially private data. Local differential privacy is a widely used privacy notion where users add Laplacian noise to their information before submitting it to a ...

Monte Carlo rendering is a computationally intensive task, but combined with recent deep-learning based advances in image denoising it is possible to achieve high quality images in a shorter amount of time. We present a novel adaptive sampling technique ...

Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However, lacking a specific mechanism to make the network learn more about the regions containing moving ...

We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the "holy grail" of ensemble learning, an open research issue for over 30 years. Our ...

A model of a redundant repairable system of the rank structure is considered. Its time operation is determined by distributions of general form. In order to evaluate the gradient of the probability of system failure in a given time interval, the ...

A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data ...

The correlation coefficient (CC) is a well-known functional information measures used to measure the interrelationship between uncertain, fuzzy sets. The use of neutrosophic sets (NS) in decision making has been increasing in recent times. Many studies ...

The model of a redundant repairable system of the rank structure is considered. Its time operation in terms of reliability is determined by general distributions. Two modified fast simulation methods for the evaluation of the system failure ...

We bound the variance and other moments of a random vector based on the range of its realizations, thus generalizing inequalities of Popoviciu and of Bhatia and Davis concerning measures on the line to several dimensions. This is done using convex duality ...

We propose a new tool for visualizing complex, and potentially large and high-dimensional, data sets called Centroid-Encoder (CE). The architecture of the Centroid-Encoder is similar to the autoencoder neural network but it has a modified target, i.e., ...

The article presents new tools for investigating the statistical properties of the harmonic signal autocorrelation function (ACF). These tools enable identification of the ACF estimator errors in measurements in which the triggering of the ...

Deep learning (DL) training is nondeterministic and such nondeterminism was shown to cause significant variance of model accuracy (up to 10.8%). Such variance may affect the validity of the comparison of newly proposed DL techniques with baselines. To ...

This is a study of the computing power of the subtyping machine behind Kennedy and Pierce's nominal subtyping with variance. We depict the lattice of fragments of Kennedy and Pierce's type system and characterize their computing power in terms of regular,...

In this special issue, we review 14 articles published in Organization Science over the past 25 years examining large-scale collaborations (LSCs) tasked with knowledge dissemination and innovation. LSCs involve sizeable pools of participants carrying out ...

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy and training efficiency. Hence, multiple identical training runs (e.g., identical training data, algorithm, and network) produce different models with different ...

Electromigration (EM) degradation evolves slowly towards failure, over a period of years. This is why EM checking methods use effective current models to represent the underlying circuit workload, which are typically constant (DC) currents over time. ...

We analyze the variance of single-run unbiased stochastic derivative estimators. The distribution of a specific conditional expectation characterizes an intrinsic distributional property of the derivative estimators in a given class, which, in turn, ...

The variance is a statistical measure frequently used for analysis of dispersion in the data. This paper presents new types of variances that use Bonferroni means and ordered weighted averages in the aggregation process of the variance. The main ...

Monte Carlo methods (MC) and refined descriptive sampling (RDS) are sampling methods that can be used to produce input values for estimation of expectation of function of output variables. This paper gives an application of RDS method in a two-dimensional ...

We present a new passive autofocusing algorithm based on fuzzy transforms. In a previous work (Roh et al., 2016) a localised variation of the variance operator is proposed based on the concept of fuzzy subspaces of the image: fuzzy C-means and conditional ...