Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Lenka Zdeborová"'
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 1, p e1010813 (2023)
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stor
Externí odkaz:
https://doaj.org/article/e8652efcaa314a0ea8d3305132cce8fc
Publikováno v:
Physical Review X, Vol 13, Iss 3, p 031021 (2023)
The cavity method is one of the cornerstones of the statistical physics of disordered systems such as spin glasses and other complex systems. It is able to analytically and asymptotically exactly describe the equilibrium properties of a broad range o
Externí odkaz:
https://doaj.org/article/8a9d6f6ed1b54944a4ca47ddc76f2107
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035033 (2023)
In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curv
Externí odkaz:
https://doaj.org/article/42957d087507457e8ac4fe235786ff15
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 2, p 025029 (2023)
Being able to reliably assess not only the accuracy but also the uncertainty of models’ predictions is an important endeavor in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty
Externí odkaz:
https://doaj.org/article/26c1efbe1d28452ab5d3494938f78da9
Autor:
Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, Lenka Zdeborová
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015019 (2023)
One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher–student perceptron on i.i.d. Gaussian inputs. Both Bayes-optim
Externí odkaz:
https://doaj.org/article/0e933c7c1af445c798d726f853b9ee0a
Publikováno v:
Physical Review X, Vol 10, Iss 4, p 041044 (2020)
Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The types of data where these networks are most successful, such as image
Externí odkaz:
https://doaj.org/article/1a23fabc856f4ecb8c6ed721b923a393
Autor:
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Publikováno v:
Physical Review X, Vol 10, Iss 1, p 011057 (2020)
Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work, we carry out an analytic study of the performance of the algorithm most commonly considered in p
Externí odkaz:
https://doaj.org/article/5be1ae79a5f941e3bce7b8ede2ac53b1
Publikováno v:
Physical Review X, Vol 9, Iss 1, p 011020 (2019)
An algorithmically hard phase is described in a range of inference problems: Even if the signal can be reconstructed with a small error from an information-theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficientl
Externí odkaz:
https://doaj.org/article/31c6a61f0b4a45fa90a1ea8691e55b9d
Publikováno v:
Journal of Statistical Mechanics: Theory and Experiment, 2022 (8)
Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervised representation learning, dictionary learning or sparse coding. We consider a setting where the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aea2cf8bb034d396784c62244f43b621
https://hdl.handle.net/20.500.11850/564870
https://hdl.handle.net/20.500.11850/564870
Publikováno v:
SIAM Journal on Mathematics of Data Science. 2:872-900
Modularity based community detection encompasses a number of widely used, efficient heuristics for identification of structure in networks. Recently, a belief propagation approach to modularity optimization provided a useful guide for identifying non
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e39971cece047eec0865304e33005734
https://doi.org/10.1137/19m1279812
https://doi.org/10.1137/19m1279812