Data Science @ LHC 2015 Workshop

Session

Symposium

9 Nov 2015, 14:00

222/R-001 (CERN)

Conveners

Symposium

• Jean-Roch Vlimant (California Institute of Technology (US))

Symposium

• Jean-Roch Vlimant (California Institute of Technology (US))

Symposium

• Jean-Roch Vlimant (California Institute of Technology (US))

Symposium

• Jean-Roch Vlimant (California Institute of Technology (US))

39. Deep Learning RNNaissance

Juergen Schmidhuber (IDSIA)

09/11/2015, 14:00

In recent years, our deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. They are now widely used in industry. I will briefly review deep supervised / unsupervised / reinforcement learning, and discuss the latest state of the art results in numerous applications. Bio : Since age 15 or so, Prof. Jürgen...

40. Better Cities through Imaging

Gregory Dobler (NYU CUSP)

10/11/2015, 14:00

I will describe how persistent, synoptic imaging of an urban skyline can be used to better understand a city, in analogy to the way persistent, synoptic imaging of the sky can be used to better understand the heavens. At the newly created Urban Observatory at the Center for Urban Science and Progress (CUSP), we are combining techniques from the domains of astronomy, computer vision, remote...

41. Scalable Gaussian Processes and the search for exoplanets

Daniel ForemanMackey (University of Washington)

11/11/2015, 14:00

Gaussian Processes are a class of non-parametric models that are often used to model stochastic behavior in time series or spatial data. A major limitation for the application of these models to large datasets is the computational cost. The cost of a single evaluation of the model likelihood scales as the third power of the number of data points. In the search for transiting exoplanets, the...

43. High-dimensional model estimation and model selection

Christian Mueller (Simons Foundation)

12/11/2015, 14:00

I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix...