no code implementations • 18 Apr 2022 • Iván A. Davidovich, Yasser Roudi
We discuss the implications of this result for the neural code in the V1 and offer the approach discussed here as a framework that future studies, perhaps exploring larger ranges of data, can employ as their starting point to examine power-law scalings in neural data.
no code implementations • 1 Feb 2022 • Matteo Marsili, Yasser Roudi
This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance.
no code implementations • 29 Mar 2021 • Nicola Bulso, Yasser Roudi
We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distributions over binary variables depends on the hidden node activation function and on the number of hidden nodes.
no code implementations • 1 Mar 2019 • Nicola Bulso, Matteo Marsili, Yasser Roudi
We investigate the complexity of logistic regression models which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997).
1 code implementation • 3 Sep 2018 • Ryan John Cubero, Matteo Marsili, Yasser Roudi
We show that the codes that achieve optimal compression in MDL are critical in a very precise sense.
Methodology Data Analysis, Statistics and Probability
no code implementations • 28 Jul 2016 • Ludovica Bachschmid-Romano, Claudia Battistin, Manfred Opper, Yasser Roudi
We first briefly consider the variational approach based on minimizing the Kullback-Leibler divergence between independent trajectories and the real ones and note that this approach only coincides with the mean field equations from the saddle point approximation to the generating functional when the dynamics is defined through a logistic link function, which is the case for the kinetic Ising model with parallel update.
no code implementations • 3 Mar 2016 • Nicola Bulso, Matteo Marsili, Yasser Roudi
We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available.
no code implementations • 1 Jun 2015 • Yasser Roudi, Graham Taylor
Learning and inferring features that generate sensory input is a task continuously performed by cortex.
no code implementations • 9 Jun 2011 • John Hertz, Yasser Roudi, Joanna Tyrcha
Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of.