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Computation Over Gaussian Networks With Orthogonal Components

Publications associées (32)

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Historically speaking, the notion of the type was reintroduced to the larger architectural discourse as a direct consequence of the crisis of the Modern. The task of revisiting the forms of the past also dictated the return of architectural methods that ha ...

2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ekaterina Krymova, Nicola Parolini, Andrea Kraus, David Kraus, Daniel Lopez, Markus Scholz, Tao Sun

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

Comment on “Origin of water in the Badain Jaran Desert, China: New insight from isotopes” by Wu et al. (2017)

David Andrew Barry, Jiaqi Chen

Precipitation isotope data were used to determine the origin of groundwater in the Badain Jaran Desert (BJD) in the study of Wu et al. (2017). Both precipitation and its isotope composition vary seasonally, so arithmetic averages of precipitation isotope v ...

2018

Expectation propagation in the large data limit

Guillaume Philippe Ivan Joseph Dehaene

Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications ...

Wiley2018

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We are interested in decompositions xnf1f2 of the majority function over n odd arguments x1, ...,xnsuch that f1and f2do not depend on xn. In this paper, we derive the conditions for f1and f2that satisfy the decomposition. Such decompositions play a centr ...

2018

Estimates of the temperature flux-temperature gradient relation above a sea floor

Andrea Cimatoribus

The relation between the flux of temperature (or buoyancy), the vertical temperature gradient and the height above the bottom is investigated in an oceanographic context, using high-resolution temperature measurements. The model for the evolution of a stra ...

Cambridge University Press2016

Implementing Higher-Kinded Types in Dotty

Martin Odersky, Guillaume André Fradji Martres, Dmytro Petrashko

dotty is a new, experimental Scala compiler based on DOT, the calculus of Dependent Object Types. Higher-kinded types are a natural extension of first-order lambda calculus, and have been a core construct of Haskell and Scala. As long as such types are jus ...

ACM2016

Function Computation over Networks

Chien-Yi Wang

This thesis looks at efficient information processing for two network applications: content delivery with caching and collecting summary statistics in wireless sensor networks. Both applications are studied under the same paradigm: function computation ove ...

EPFL2015