Latest Publications

New publications

Universal Approximation in Dropout Neural Networks

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We have submitted Universal Approximation in Dropout Neural Networks, and it is currently under review. This is joint work between, Oxana Manita, Mark Peletier, Jacobus Portegies, Albert Senen-Cerda and myself. A preprint is available on arXiv.

Dropout and Dropconnect on a shallow neural network

Asymptotic convergence rate of Dropout on shallow linear neural networks

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Modeling Rydberg Gases using Random Sequential Adsorption on Random Graphs

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Example random neural network when using Dropconnect

Almost Sure Convergence of Dropout Algorithms for Neural Networks

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Schematic depiction of the hitting time for the Schatten p norm

Markov chains for error accumulation in quantum circuits

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Latest News

News

Open Competition ENW-KLEIN-1 grant

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I have recently been awarded an NWO Open Competition ENW-Klein-1 grant. The topic is Clustering and Spectral Concentration in Markov Chains.

ICML 2020 logo
News

ICML 2020

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This week I attended ICML 2020 online because of COVID-19. I applaud the ICML: the digital experience was good and the speakers recorded excellent videos.

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Announcing NExTWORKx

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Pierre Brémaud – Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding

Poster at Stochastic Networks 2018

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article arxiv asymptotics Block Markov Chains clustering Markov chain mathematics networks physics TU Delft
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