🗞News direttamente da #Meta!🤩
È già da un po’ che possiamo capirci di più sulle #ads Meta grazie a Perché vedo questo annuncio?, ma con l’aggiornamento di oggi possiamo andare ancora più nel dettaglio🕵️♂️.
Ora sono disponibili nuove informazioni su come i modelli di #machinelearning di Meta sfruttino i dati delle attività in-app e su siti web ed app esterne, per mostrare gli annunci agli utenti.
Inoltre, si amplia il centro di controllo Preferenze annunci, dove è possibile gestire e modificare i nostri interessi per visualizzare inserzioni sempre più pertinenti.
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Продолжая тему нестандартного использования стандартных каналов коммуникации. Туристический баннер в Хельсинки, 🇫🇮
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Прекрасный пример ‘copy-driven’ коммуникации, построенной на тексте.
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@planning_horizon
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#machinelearning
A nice colloquium paper:
The unreasonable effectiveness of deep learning in artificial intelligence | PNAS
https://www.pnas.org/content/117/48/30033
#machinelearning
https://arxiv.org/abs/2007.04504
Learning Differential Equations that are Easy to Solve
Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud
Differential equations parameterized by neural networks become expensive to solve numerically as training progresses. We propose a remedy that encourages learned dynamics to be easier to solve. Specifically, we introduce a differentiable surrogate for the time cost of standard numerical solvers, using higher-order derivatives of solution trajectories. These derivatives are efficient to compute with Taylor-mode automatic differentiation. Optimizing this additional objective trades model performance against the time cost of solving the learned dynamics. We demonstrate our approach by training substantially faster, while nearly as accurate, models in supervised classification, density estimation, and time-series modelling tasks.