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Mathematicians from RUDN University and the Free University of Berlin proposed a new way of using neural networks for working with noisy high-dimensional data

Mathematicians from RUDN University and the Free University of Berlin proposed a new way of using neural networks for working with noisy high-dimensional data

Mathematicians from RUDN University and the Free University of Berlin have proposed a new approach to studying the probability distributions of observed data using artificial neural networks. The new approach works better with so-called outliers, i.e. input data objects that deviate significantly from the overall sample.

The restoration of the probability distribution of observed data by artificial neural networks is the most important part of machine learning. The probability distribution not only allows us to predict the behaviour of the system under study, but also to quantify the uncertainty with which forecasts are made. The main difficulty is that, as a rule, only the data are observed, but their exact probability distributions are not available. To solve this problem, Bayesian and other similar approximate methods are used. But their use increases the complexity of a neural network and therefore makes its training more complicated.

RUDN University and the Free University of Berlin mathematicians used deterministic weights in neural networks, which would help overcome the limitations of Bayesian methods. They developed a formula that allows one to correctly estimate the variance of the distribution of observed data. The proposed model was tested on different data: synthetic and real; on data containing outliers and on data from which the outliers were removed. The new method allows restoration of probability distributions with accuracy previously unachievable.

The mathematicians of RUDN University and the Free University of Berlin used deterministic weights for neural networks and used the networks outputs to encode the distribution of latent variables for the desired marginal distribution. An analysis of the training dynamics of such networks allowed them to obtain a formula that correctly estimates the variance of observed data, despite the presence of outliers in the data. The proposed model was tested on different data: synthetic and real. The new method allows restoring probability distributions with higher accuracy compared with other modern methods. Accuracy was assessed using the AUC method (area under the curve is the area under the graph that allows making assessment of the mean square error of the predictions depending on the sample size estimated by the network as “reliable”; the higher the AUC score, the better the predictions).

The article was published in the journal Artificial Intelligence.

30 Jan 2018
The conference on international arbitration, where law students from European universities simulate court proceedings and alternately defend the interests of the respondent and the orator.
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03 Nov 2017
The main goal of the RUDN University and UNISDR Office for Northeast Asia and Global Education and Training Institute for Disaster Risk Reduction at Incheon (UNISDR ONEA-GETI) cooperation is to obtain knowledge about disaster risk reduction and international experience in this area for creating training courses for basic and additional professional education in RUDN
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30 Dec
Biologists from RUDN University discovered the secret of flaxseed oil with long shelf life

Biologists from RUDN University working together with their colleagues from the Institute of Molecular Biology of the Russian Academy of Sciences and the Institute of Flax studied the genes that determine the fatty acid composition in flaxseed oil and identified polymorphisms in six of them. The team also found out what gene variations could extend the shelf life of flaxseed oil. This data can be used to improve the genetic selection of new flax breeds. The results were published in the BMC Plant Biology journal.

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11 Oct
Mowing Is More Harmful to Soil Than Grazing

A team of biologists analyzed soil samples from a pasture and a regularly mowed meadow and found out that grazing lets more carbon get into the soil than mowing. This, in turn, improves the carbon cycle and makes microorganisms more efficient.

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11 Oct
Soil scientists suggest method for remediating urban garden soils contaminated with lead and arsenic

The soils of urban gardens and vegetable patches contain a lot of toxicants (including lead and arsenic) in high concentrations which can be harmful to the health of children and people with chronic diseases. A team of soil scientists from RUDN University suggested a remediation method developed based on data collected in a garden of Brooklyn (NY, U.S.).

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