Events
January 2020 Monday Tuesday Wednesday Thursday Friday Saturday Sunday December 30, 2019 December 31, 2019 January 1, 2020 January 2, 2020 January 3, 2020 January 4, 2020 January 5, 2020 January 6, 2020 January 7, 2020 January 8, 2020 January 9, 2020
Keynote: Radu Horaud
Keynote: Radu HoraudJanuary 9, 2020 –
January 10, 2020 January 11, 2020 January 12, 2020 January 13, 2020 January 14, 2020 January 15, 2020 January 16, 2020
Capacity of a LoRaWAN cell, by Martin Heusse (Drakkar)Capacity of a LoRaWAN cell, by Martin Heusse (Drakkar)January 16, 2020 –
Bâtiment IMAG (406)Saint-Martin-d'Hères, 38400FranceWe propose a model to estimate the packet delivery rate in a LoRaWAN cell, when all nodes have the same traffic generation process and may use repetitions. The model predicts the transmission success rate for any cell range and node density, with similar traffic from all nodes. We find that the transmission success depends on striking a balance between the adverse effects of attenuation and collisions; in small cells, it is highly dependent on the suitable allocation of spreading factors, whereas using packet repetitions is more effective in large cells.
January 17, 2020 January 18, 2020 January 19, 2020 January 20, 2020 January 21, 2020 January 22, 2020 January 23, 2020
Can random matrices change the future of machine learning? by Romain Couillet (Gipsa)Can random matrices change the future of machine learning? by Romain Couillet (Gipsa)January 23, 2020 –
Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes)
Title: Can random matrices change the future of machine learning?
Abstract: Many standard machine learning algorithms and intuitions are known to misbehave, if not dramatically collapse, when operated on large dimensional data. In this talk, we will show that large dimensional statistics, and particularly random matrix theory, not only can elucidate this behavior but provides a new set of tools to understand and (sometimes drastically) improve machine learning algorithms. Besides, we will show that our various theoretical findings are provably applicable to very realistic and not-so-large dimensional data.January 24, 2020 January 25, 2020 January 26, 2020 January 27, 2020 January 28, 2020 January 29, 2020 January 30, 2020
HDR Nicolas Gast
HDR Nicolas GastJanuary 30, 2020 –
Bâtiment IMAG (amphitheater)Saint-Martin-d'Hères, 38400FranceTBA
January 31, 2020 February 1, 2020 February 2, 2020
