The results most commonly found in the literature prove that neural networks approximate functions with classical smoothness to the same accuracy as classical linear methods of approximation, e. g. approximation by polynomials or by piecewise polynomials on prescribed partitions.
no code implementations • 2 Oct 2014 • L. Jacques, C. De Vleeschouwer, Y. Boursier, P. Sudhakar, C. De Mol, A. Pizurica, S. Anthoine, P. Vandergheynst, P. Frossard, C. Bilen, S. Kitic, N. Bertin, R. Gribonval, N. Boumal, B. Mishra, P. -A. Absil, R. Sepulchre, S. Bundervoet, C. Schretter, A. Dooms, P. Schelkens, O. Chabiron, F. Malgouyres, J. -Y. Tourneret, N. Dobigeon, P. Chainais, C. Richard, B. Cornelis, I. Daubechies, D. Dunson, M. Dankova, P. Rajmic, K. Degraux, V. Cambareri, B. Geelen, G. Lafruit, G. Setti, J. -F. Determe, J. Louveaux, F. Horlin, A. Drémeau, P. Heas, C. Herzet, V. Duval, G. Peyré, A. Fawzi, M. Davies, N. Gillis, S. A. Vavasis, C. Soussen, L. Le Magoarou, J. Liang, J. Fadili, A. Liutkus, D. Martina, S. Gigan, L. Daudet, M. Maggioni, S. Minsker, N. Strawn, C. Mory, F. Ngole, J. -L. Starck, I. Loris, S. Vaiter, M. Golbabaee, D. Vukobratovic
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions.