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Probabilistic Deep Learning on Spheres for Weather/Climate Applications

Related publications (38)

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Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Design considerations for implantable and wearable antennas

Anja Skrivervik

This contribution gives a review of our results characterizing the propagation in lossy biological tissues. These results were mainly obtained using a simplified spherical model allowing a description in spherical wave decomposition. This model is of cours ...

IEEE2017

Examining Building Design Decisions Under Long Term Weather Variability and Microclimatic Effects: A case-based exploratory study

Marilyne Andersen, Luisa Pastore, Parag Rastogi, Margaux Marie Valérie Peltier, Minu Agarwal

Thermal building simulation currently uses Typical Meteorological Year (TMY) data to guide the design decision-making process or for compliance with energy standards. TMY data usually excludes extremes and in many cases are gathered from microclimatic cont ...

2016

How is the Weather: Automatic Inference from Images

Martin Vetterli, Guillermo Barrenetxea Kobas, Albrecht Johannes Lindner, Feng Yang, Zichong Chen

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to autom ...

2012

Energy Efficient Building Climate Control using Stochastic Model Predictive Control and Weather Predictions

Colin Neil Jones

One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an importan ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

EPFL2008

Machine Learning for Information Retrieval

David Grangier

École Polytechnique Fédérale de Lausanne2008

Machine Learning for Information Retrieval

David Grangier

IDIAP2008

A Distance Model for Rhythms

Samy Bengio, Jean-François Paiement

Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...

2008

A Distance Model for Rhythms

Samy Bengio, Jean-François Paiement

IDIAP2008