arXiv subjects

Samir Perlaza

Publications and source records attributed to Samir Perlaza.

4 recordsLinked to original sources

arXiv2026

On the Role of Reversible Instance Normalization

Data normalization is a crucial component of deep learning models, yet its role in time series forecasting remains insufficiently understood. In this paper, we identify three central challenges for normalization in time series forecasting: temporal input distribution shift, spatial input distribution shift, and conditional output distribution shift. In this context, we revisit the widely used Reversible Instance Normalization (RevIN), by showing through ablation studies that several of its components are redundant or even detrimental. Based on these observations, we draw new perspectives to improve RevIN's robustness and generalization.

cs.LG↗

arXiv2020

An Upper Bound on the Error Induced by Saddlepoint Approximations -- Applications to Information Theory

This paper introduces an upper bound on the absolute difference between: (a) the cumulative distribution function (CDF) of the sum of a finite number of independent and identically distributed random variables with finite absolute third moment; and (b) a saddlepoint approximation of such CDF. This upper bound, which is particularly precise in the regime of large deviations, is used to study the dependence testing (DT) bound and the meta converse (MC) bound on the decoding error probability (DEP) in point-to-point memoryless channels. Often, these bounds cannot be analytically calculated and thus lower and upper bounds become particularly useful. Within this context, the main results include, respectively, new upper and lower bounds on the DT and MC bounds. A numerical experimentation of these bounds is presented in the case of the binary symmetric channel, the additive white Gaussian noise channel, and the additive symmetric $α$-stable noise channel.

cs.IT↗

arXiv2019

Recovery of Missing Data in Correlated Smart Grid Datasets

We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring and control of the electricity distribution system. Each dataset is described by a low rank matrix. Different datasets are correlated as a result of containing measurements of different physical magnitudes generated by the same distribution system. To assess the validity of matrix completion techniques in the recovery of missing data, we characterize the fundamental limits when two correlated datasets are jointly recovered. We then proceed to evaluate the performance of Singular Value Thresholding (SVT) and Bayesian SVT (BSVT) in this setting. We show that BSVT outperforms SVT by simulating the recovery for different correlated datasets. The performance of BSVT displays the tradeoff behaviour described by the fundamental limit, which suggests that BSVT exploits the correlation between the datasets in an efficient manner.

eess.SP↗

arXiv2012

Small Cell Deployments: Recent Advances and Research Challenges

This paper summarizes the outcomes of the 5th International Workshop on Femtocells held at King's College London, UK, on the 13th and 14th of February, 2012.The workshop hosted cutting-edge presentations about the latest advances and research challenges in small cell roll-outs and heterogeneous cellular networks. This paper provides some cutting edge information on the developments of Self-Organizing Networks (SON) for small cell deployments, as well as related standardization supports on issues such as carrier aggregation (CA), Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell Interference Coordination (eICIC), etc. Furthermore, some recent efforts on issues such as energy-saving as well as Machine Learning (ML) techniques on resource allocation and multi-cell cooperation are described. Finally, current developments on simulation tools and small cell deployment scenarios are presented. These topics collectively represent the current trends in small cell deployments.

cs.NI↗