Zobrazeno 1 - 10
of 393
pro vyhledávání: '"POPESCU, Ionel"'
Autor:
Petrica, Marian, Popescu, Ionel
In this paper we cover a few topics on how to treat inverse problems. There are two different flows of ideas. One approach is based on Morse Lemma. The other is based on analyticity which proves that the number of solutions to the inverse problems is
Externí odkaz:
http://arxiv.org/abs/2408.14616
In this paper, we prove that in the overparametrized regime, deep neural network provide universal approximations and can interpolate any data set, as long as the activation function is locally in $L^1(\RR)$ and not an affine function. Additionally,
Externí odkaz:
http://arxiv.org/abs/2304.10552
In this paper we study probabilistic and neural network approximations for solutions to Poisson equation subject to Holder data in general bounded domains of $\mathbb{R}^d$. We aim at two fundamental goals. The first, and the most important, we show
Externí odkaz:
http://arxiv.org/abs/2209.01432
Autor:
Duan, JunTao, Popescu, Ionel
Minimum-variance portfolio optimizations rely on accurate covariance estimator to obtain optimal portfolios. However, it usually suffers from large error from sample covariance matrix when the sample size $n$ is not significantly larger than the numb
Externí odkaz:
http://arxiv.org/abs/2204.00204
Autor:
Petrica, Marian, Popescu, Ionel
In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between
Externí odkaz:
http://arxiv.org/abs/2203.00407
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. We in this work introduce an iterative algorithm 'Concent' that actively eliminate this bias and re
Externí odkaz:
http://arxiv.org/abs/2201.00230
Johnson-Lindenstrauss guarantees certain topological structure is preserved under random projections when project high dimensional deterministic vectors to low dimensional vectors. In this work, we try to understand how random matrix affect norms of
Externí odkaz:
http://arxiv.org/abs/2112.00300
Johnson-Lindenstrauss lemma states random projections can be used as a topology preserving embedding technique for fixed vectors. In this paper, we try to understand how random projections affect probabilistic properties of random vectors. In particu
Externí odkaz:
http://arxiv.org/abs/2106.14825
In this paper we propose a three stages analysis of the evolution of Covid19 in Romania. There are two main issues when it comes to pandemic prediction. The first one is the fact that the numbers reported of infected and recovered are unreliable, how
Externí odkaz:
http://arxiv.org/abs/2007.13494
Analysing and understanding the transmission and evolution of the COVID-19 pandemic is mandatory to be able to design the best social and medical policies, foresee their outcomes and deal with all the subsequent socio-economic effects. We address thi
Externí odkaz:
http://arxiv.org/abs/2006.12926