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This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...
Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...
This work focuses on obtaining a magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T-1 and transverse T-2* relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametr ...
We consider the robust linear regression problem in the online setting where we have access to the data in a streaming manner, one data point after the other. More specifically, for a true parameter , we consider the corrupted Gaussian linear model $ y=\la ...
Several non-linear behaviors in scales space, in the framework of Scale Relativity Theory, are highlighted. All these are possible through the employment of fractal-type Airy functions, which allow a revaluation of the wave/corpuscle duality, from the pers ...
In the present paper the "interface" dynamics in the case of two complex systems interaction, assimilated to fractal-type mathematical objects, are analyzed. In such context, fractal bistable-type behaviors as transitions in the scale space are obtained. O ...
The benefits and limitations inherent to the 2D post-processing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estim ...
In the present paper the "interface" dynamics in the case of two complex systems interaction, assimilated to fractal-type mathematical objects, are analyzed. In such context, fractal bistable-type behaviors as transitions in the scale space are obtained. O ...
Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow net-works, which tend to get stuck in badly-generalizing loca ...
Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...