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Found 21 papers, 5 papers with code
Extracting individual variable information for their decoupling, direct mutual information and multi-feature Granger causality
Working with multiple variables they usually contain difficult to control complex dependencies.
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals
Electroencephalography (EEG) signals are resultants of extremely complex brain activity.
Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
To avoid such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e. g. $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln(\rho_\theta (x_\tau))$ moving log-likelihood, evolving in time.
Predicting probability distributions for cancer therapy drug selection optimization
We are mostly interested in the best drug in their batch to be tested - proper optimization of their selection for extreme statistics requires knowledge of the entire probability distributions, which for distributions of drug properties among cell lines often turn out binomial, e. g. depending on corresponding gene.
Low cost prediction of probability distributions of molecular properties for early virtual screening
While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles.
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 only allows to predict conditional probability distributions, with capabilities bounded by conditional entropy $H(Y|X)$.
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 learning, allowing to optimize basis for a given matrix.
Exploiting context dependence for image compression with upsampling
The presented simple inexpensive general methodology can be also used for different types of data like DCT coefficients in lossy image compression.
Adaptive exponential power distribution with moving estimator for nonstationary time series
While standard estimation assumes that all datapoints are from probability distribution of the same fixed parameters $\theta$, we will focus on maximum likelihood (ML) adaptive estimation for nonstationary time series: separately estimating parameters $\theta_T$ for each time $T$ based on the earlier values $(x_t)_{t<T}$ using (exponential) moving ML estimator $\theta_T=\arg\max_\theta l_T$ for $l_T=\sum_{t<T} \eta^{T-t} \ln(\rho_\theta (x_t))$ and some $\eta\in(0, 1]$.
SGD momentum optimizer with step estimation by online parabola model
It is done by estimating linear trend of gradients $\vec{g}=\nabla F(\vec{\theta})$ in $\hat{v}$ direction: such that $g(\vec{\theta}_\bot+\theta\hat{v})\approx \lambda (\theta -p)$ for $\theta = \vec{\theta}\cdot \hat{v}$, $g= \vec{g}\cdot \hat{v}$, $\vec{\theta}_\bot=\vec{\theta}-\theta\hat{v}$.
Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions
Deep neural networks are usually trained with stochastic gradient descent (SGD), which minimizes objective function using very rough approximations of gradient, only averaging to the real gradient.
Credibility evaluation of income data with hierarchical correlation reconstruction
In situations like tax declarations or analyzes of household budgets we would like to automatically evaluate credibility of exogenous variable (declared income) based on some available (endogenous) variables - we want to build a model and train it on provided data sample to predict (conditional) probability distribution of exogenous variable based on values of endogenous variables.
Gaussian AutoEncoder
The original Variational AutoEncoder (VAE) uses randomness in encoder - causing problematic distortion, and overlaps in latent space for distinct inputs.
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction
While we are usually focused on forecasting future values of time series, it is often valuable to additionally predict their entire probability distributions, e. g. to evaluate risk, Monte Carlo simulations.
Hierarchical correlation reconstruction with missing data, for example for biology-inspired neuron
Machine learning often needs to model density from a multidimensional data sample, including correlations between coordinates.
Polynomial-based rotation invariant features
One of basic difficulties of machine learning is handling unknown rotations of objects, for example in image recognition.
Rapid parametric density estimation
There will be discussed inexpensive density estimation, for example literally fitting a polynomial (or Fourier series) to the sample, which coefficients are calculated by just averaging monomials (or sine/cosine) over the sample.
Joint error correction enhancement of the fountain codes concept
This approach requires a priori knowledge of the final damage level of every packet - insufficient redundancy leads to packet loss, overprotection means suboptimal channel rate.
Information Theory Information Theory
Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding
The latter uses nearly exact probabilities - easily approaching theoretical compression rate limit (Shannon entropy), but at cost of much larger computational cost.
Information Theory Information Theory
Embedding grayscale halftone pictures in QR Codes using Correction Trees
We will discuss a general problem of using codes with chosen statistical constrains, for example reproducing given grayscale picture using halftone technique.
Information Theory Cryptography and Security Multimedia Information Theory
Asymmetric numeral systems
It has some similarities to Range Coding but instead of encoding symbol in choosing a range, we spread these ranges uniformly over the whole interval.
Information Theory Cryptography and Security General Mathematics Information Theory
