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Top Artificial Intelligence Influencers To Follow in 2019 MarkTechPost

#artificialintelligence

Yoshua Bengio: Yoshua BengioOCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadiancognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


Top Artificial Intelligence Influencers To Follow in 2020 MarkTechPost

#artificialintelligence

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


Top Artificial Intelligence Influencers To Follow in 2020 MarkTechPost

#artificialintelligence

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


Assassin's Creed creators pledge €500,000 to Notre Dame

The Guardian

Video game creators at Ubisoft Montréal – the development studio that rebuilt 18th-century Paris in its 2014 historical action game Assassin's Creed Unity – have joined the global outpouring of grief in the wake of Monday's devastating fire at Notre Dame Cathedral. Ubisoft will be donating €500,000 to help with restoration efforts, and is also making Assassin's Creed Unity available free on PC for the next week, "giving everyone the chance to experience the majesty and beauty of Notre Dame the best way we know how", said a studio spokesperson. "We hope, with this small gesture, we can provide everyone an opportunity to appreciate our virtual homage to this monumental piece of architecture." Caroline Miousse, a level artist on the game, spent 14 months working almost exclusively on the cathedral, inside and out. It is furnished and decorated as it would have been in 1790, down to the paintings hanging on the walls.


DeepMoD: Deep learning for Model Discovery in noisy data

arXiv.org Machine Learning

Institut Curie, PSL Research University, CNRS UMR168, Paris, France We introduce DeepMoD, a deep learning based model discovery algorithm which seeks the partial differential equation underlying a spatiotemporal data set. DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives. A feed-forward neural network approximates the data set and automatic differentiation is used to construct this function library and perform regression within the neural network. This construction makes it extremely robust to noise and applicable to small data sets and, contrary to other deep learning methods, does not require a training set and is impervious to overfitting. We illustrate this approach on several physical problems, such as the Burgers', Korteweg-de Vries, advection-diffusion and Keller-Segel equations, and This resilience to noise and high performance at very few samples highlights the potential of this method to be applied on experimental data. The increasing ability to generate large amounts of model discovery turns into finding a sparse representation data from complex physical, biological, chemical and social of the coefficient vector ξ. Rudy et al. [3] introduce the systems is beginning to transform quantitative science.regression