Zobrazeno 1 - 10
of 34
pro vyhledávání: '"El-Laham, Yousef"'
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shifts. Existing datasets may not encompass the full range of statistical
Externí odkaz:
http://arxiv.org/abs/2406.05249
Autor:
Potluru, Vamsi K., Borrajo, Daniel, Coletta, Andrea, Dalmasso, Niccolò, El-Laham, Yousef, Fons, Elizabeth, Ghassemi, Mohsen, Gopalakrishnan, Sriram, Gosai, Vikesh, Kreačić, Eleonora, Mani, Ganapathy, Obitayo, Saheed, Paramanand, Deepak, Raman, Natraj, Solonin, Mikhail, Sood, Srijan, Vyetrenko, Svitlana, Zhu, Haibei, Veloso, Manuela, Balch, Tucker
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide
Externí odkaz:
http://arxiv.org/abs/2401.00081
Stochastic differential equations (SDEs) have been widely used to model real world random phenomena. Existing works mainly focus on the case where the time series is modeled by a single SDE, which might be restrictive for modeling time series with di
Externí odkaz:
http://arxiv.org/abs/2312.13152
Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application in the other domains remains limited. This paper proposes a Mixup regu
Externí odkaz:
http://arxiv.org/abs/2312.13141
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled. Whilst traditional imputation methods often impose strong assumptions on the underlying data
Externí odkaz:
http://arxiv.org/abs/2307.00868
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process follows that
Externí odkaz:
http://arxiv.org/abs/2306.07235
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine lea
Externí odkaz:
http://arxiv.org/abs/2211.11513
Autor:
El-Laham, Yousef, Vyetrenko, Svitlana
Neural style transfer is a powerful computer vision technique that can incorporate the artistic "style" of one image to the "content" of another. The underlying theory behind the approach relies on the assumption that the style of an image is represe
Externí odkaz:
http://arxiv.org/abs/2209.11306
Autor:
Fons, Elizabeth, Sztrajman, Alejandro, El-laham, Yousef, Iosifidis, Alexandros, Vyetrenko, Svitlana
Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general approximators has been shown in a wide variety of data sources, with ap
Externí odkaz:
http://arxiv.org/abs/2208.05836
Publikováno v:
Proceedings of the IEEE, 110(4):404--453, April 2022
Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous ran
Externí odkaz:
http://arxiv.org/abs/2202.11633