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Found 12 papers, 0 papers with code
Interpolation property of shallow neural networks
We study the geometry of global minima of the loss landscape of overparametrized neural networks.
From Monte Carlo to neural networks approximations of boundary value problems
As a second goal, we show that the obtained Monte Carlo solver renders { in a constructive way} ReLU deep neural network (DNN) solutions to Poisson problem, whose sizes depend at most polynomialy in the dimension $d$ and in the desired error.
LoCoV: low dimension covariance voting algorithm for portfolio optimization
Minimum-variance portfolio optimizations rely on accurate covariance estimator to obtain optimal portfolios.
Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks
Lastly we use the ensemble to get estimates of the parameters from the real data of Covid19 in Romania and then we illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths.
Recover the spectrum of covariance matrix: a non-asymptotic iterative method
It is well known the sample covariance has a consistent bias in the spectrum, for example spectrum of Wishart matrix follows the Marchenko-Pastur law.
Invariance principle of random projection for the norm
In particular we prove the distribution of the norm of random vector $X \in \mathbb{R}^n$, whose entries are i. i. d.
A regime switching on Covid19 analysis and prediction in Romania
The second issue is that there were many factors which affected the evolution of the pandemic.
A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania
More specifically, we want to statistically estimate all the relevant parameters for the new coronavirus COVID-19, such as the reproduction number, fatality rate or length of infectiousness period, based on Romanian patients, as well as be able to predict future outcomes.
A cost-reducing partial labeling estimator in text classification problem
We propose a new approach to address the text classification problems when learning with partial labels is beneficial.
Naive Bayes with Correlation Factor for Text Classification Problem
Naive Bayes estimator is widely used in text classification problems.
Averaging plus Learning Models and Their Asymptotics
We develop original models to study interacting agents in financial markets and in social networks.
