AI for social good: unlocking the opportunity for positive impact

Autor: Nenad Tomašev, Julien Cornebise, Frank Hutter, Shakir Mohamed, Angela Picciariello, Bec Connelly, Danielle C. M. Belgrave, Daphne Ezer, Fanny Cachat van der Haert, Frank Mugisha, Gerald Abila, Hiromi Arai, Hisham Almiraat, Julia Proskurnia, Kyle Snyder, Mihoko Otake-Matsuura, Mustafa Othman, Tobias Glasmachers, Wilfried de Wever, Yee Whye Teh, Mohammad Emtiyaz Khan, Ruben De Winne, Tom Schaul, Claudia Clopath

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-6 (2020)

The AI for Social Good movement aims to apply AI/ML tools to help in delivering on the United Nations’ sustainable development goals (SDGs). Here, the authors identify the challenges and propose guidelines for designing and implementing successful

Externí odkaz: https://doaj.org/article/7387a9d909244c1892f82044204fa9ec

Efficient Bayesian inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities

Autor: Yee Whye Teh, Bryn Elesedy, Bobby He, Michael Hutchinson, Sheheryar Zaidi, Avishkar Bhoopchand, Ulrich Paquet, Nenad Tomasev, Jonathan Read, Peter J. Diggle

Publikováno v: Journal of the Royal Statistical Society Series A: Statistics in Society. 185:S65-S85

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bedabf661e416243f5a4d9074eadc8ca
https://doi.org/10.1111/rssa.12971

Authors’ Reply to the Discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 11 June 2021

Autor: Yee Whye Teh, Bryn Elesedy, Bobby He, Michael Hutchinson, Sheheryar Zaidi, Avishkar Bhoopchand, Ulrich Paquet, Ne-nad Tomasev, Jonathan Read, Peter J. Diggle

Publikováno v: Journal of the Royal Statistical Society Series A: Statistics in Society. 185:S107-S109

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a3fea4a427dee8d2b68e25a4997793f4
https://doi.org/10.1111/rssa.12976

Dual Space Preconditioning for Gradient Descent

Autor: Chris J. Maddison, Yee Whye Teh, Daniel Paulin, Arnaud Doucet

Publikováno v: Maddison, C, Paulin, D, Doucet, A & The, Y W 2021, ' Dual Space Preconditioning for Gradient Descent ', Siam journal on optimization, vol. 31, no. 1, pp. 991-1016 . https://doi.org/10.1137/19M130858X

The conditions of relative smoothness and relative strong convexity were recently introduced for the analysis of Bregman gradient methods for convex optimization. We introduce a generalized left-preconditioning method for gradient descent, and show t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e995afdf00b86ff964048f4ad2a7ee4
https://hdl.handle.net/20.500.11820/080e540d-288d-4830-b7e9-0331b5a3dfc9

An exact auxiliary variable Gibbs sampler for a class of diffusions

Autor: Qi Wang, Vinayak Rao, Yee Whye Teh

Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating paths from these processes is usually an intractable problem, and typic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7a5c4c75c1202ee928dc1238a6fbaed
https://doi.org/10.1080/10618600.2020.1816177