A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
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The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...
We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and b ...
The topic of this thesis is the development of new reconstruction methods for cryo-electron microscopy (cryo-EM). Cryo-EM has revolutionized the field of structural biology over the last decade and now permits the regular discovery of biostructures. Yet, t ...
In this supplementary material, we present the details of the neural network architecture and training settings used in all our experiments. This holds for all experiments presented in the main paper as well as in this supplementary material. We also show ...
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 ...
While a nonlinear viscosity is used widely to control oscillations when solving conservation laws using high-order elements based methods, such techniques are less straightforward to apply in global spectral methods as a local estimate of solution regulari ...
We experimentally solve the problem of maximizing capacity under a total supply power constraint in a massively parallel submarine cable context, i.e., for a spatially uncoupled system in which fiber Kerr nonlinearity is not a dominant limitation. By using ...
This study evaluates and compares several machine learning methods on the effects of different parameters in lead adsorption capacity. pH, contact time, adsorbent dosage and initial lead concentration were considered as inputs and adsorption capacity was r ...
We study the problem of distributed estimation over adaptive networks where communication delays exist between nodes. In particular, we investigate the diffusion Least-Mean-Square (LMS) strategy where delayed intermediate estimates (due to the communicatio ...
This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...