
Encoding of probability distributions for Asymmetric Numeral Systems
Many data compressors regularly encode probability distributions for ent...
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Improving distribution and flexible quantization for DCT coefficients
While it is a common knowledge that AC coefficients of Fourierrelated t...
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Exploiting context dependence for image compression with upsampling
Image compression with upsampling encodes information to succeedingly in...
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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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Nearly accurate solutions for Isinglike models using Maximal Entropy Random Walk
While onedimensional Markov processes are well understood, going to hig...
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SGD momentum optimizer with step estimation by online parabola model
In stochastic gradient descent, especially for neural network training, ...
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Parametric context adaptive Laplace distribution for multimedia compression
Data compression often subtracts predictor and encodes the difference (r...
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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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Credibility evaluation of income data with hierarchical correlation reconstruction
In situations like tax declarations or analyzes of household budgets we ...
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Gaussian AutoEncoder
Evaluating distance between sample distribution and the wanted one, usua...
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Modeling joint probability distribution of yield curve parameters
US Yield curve has recently collapsed to its most flattened level since ...
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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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Hierarchical correlation reconstruction with missing data, for example for biologyinspired neuron
Machine learning often needs to estimate density from a multidimensional...
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Hierarchical correlation reconstruction with missing data
Machine learning often needs to estimate density from a multidimensional...
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Polynomialbased rotation invariant features
One of basic difficulties of machine learning is handling unknown rotati...
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Jarek Duda
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