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Publications associées (146)
Stable parameterization of continuous and piecewise-linear functions
Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos
Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...
2023Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research
The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/d ...
Oxford2023Noncollinear DFT plus U and Hubbard parameters with fully relativistic ultrasoft pseudopotentials
The magnetic, noncollinear parametrization of Dudarev's DFT + U method is generalized to fully relativistic ultrasoft pseudopotentials. We present the definition of the DFT + U total energy functional and the calculation of forces and stresses in the case ...
College Pk2023Algebraic twists of GL(2) automorphic forms
In this thesis we consider the problem of estimating the correlation of Hecke eigenvalues of GL2 automorphic forms with a class of functions of algebraic origin defined over finite fields called trace functions. The class of trace functions is vast and inc ...
EPFL2023Finite Littlestone Dimension Implies Finite Information Complexity
Michael Christoph Gastpar, Aditya Pradeep, Ido Nachum
We prove that every online learnable class of functions of Littlestone dimension d admits a learning algorithm with finite information complexity. Towards this end, we use the notion of a globally stable algorithm. Generally, the information complexity of ...
2022