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04/06/2023
Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
The real life time series are usually nonstationary, bringing a difficul...
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09/13/2022
Predicting probability distributions for cancer therapy drug selection optimization
Large variability between cell lines brings a difficult optimization pro...
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09/06/2022
Compression Optimality of Asymmetric Numeral Systems
Compression also known as entropy coding has a rich and long history. Ho...
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07/21/2022
Low cost prediction of probability distributions of molecular properties for early virtual screening
While there is a general focus on predictions of values, mathematically ...
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06/13/2022
Predicting conditional probability distributions of redshifts of Active Galactic Nuclei using Hierarchical Correlation Reconstruction
While there is a general focus on prediction of values, real data often ...
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04/18/2022
Fast optimization of common basis for matrix set through Common Singular Value Decomposition
SVD (singular value decomposition) is one of the basic tools of machine ...
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01/13/2022
Context binning, model clustering and adaptivity for data compression of genetic data
Rapid growth of genetic databases means huge savings from improvements i...
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06/11/2021
Encoding of probability distributions for Asymmetric Numeral Systems
Many data compressors regularly encode probability distributions for ent...
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07/23/2020
Improving distribution and flexible quantization for DCT coefficients
While it is a common knowledge that AC coefficients of Fourier-related t...
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04/06/2020
Exploiting context dependence for image compression with upsampling
Image compression with upsampling encodes information to succeedingly in...
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03/04/2020
Adaptive exponential power distribution with moving estimator for nonstationary time series
While standard estimation assumes that all datapoints are from probabili...
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12/31/2019
Nearly accurate solutions for Ising-like models using Maximal Entropy Random Walk
While one-dimensional Markov processes are well understood, going to hig...
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07/16/2019
SGD momentum optimizer with step estimation by online parabola model
In stochastic gradient descent, especially for neural network training, ...
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05/28/2019
Parametric context adaptive Laplace distribution for multimedia compression
Data compression often subtracts predictor and encodes the difference (r...
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01/31/2019
Improving SGD convergence by tracing multiple promising directions and estimating distance to minimum
Deep neural networks are usually trained with stochastic gradient descen...
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12/19/2018
Credibility evaluation of income data with hierarchical correlation reconstruction
In situations like tax declarations or analyzes of household budgets we ...
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11/12/2018
Gaussian Auto-Encoder
Evaluating distance between sample distribution and the wanted one, usua...
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07/31/2018
Modeling joint probability distribution of yield curve parameters
US Yield curve has recently collapsed to its most flattened level since ...
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07/11/2018
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction
While we are usually focused on predicting future values of time series,...
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04/17/2018
Hierarchical correlation reconstruction with missing data, for example for biology-inspired neuron
Machine learning often needs to estimate density from a multidimensional...
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04/17/2018
Hierarchical correlation reconstruction with missing data
Machine learning often needs to estimate density from a multidimensional...
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01/03/2018