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  • 1.
    Albertsson, Kim
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB. CERN.
    Gleyze, Sergei
    University of Florida.
    Huwiler, Marc
    EPFL.
    Ilievski, Vladimir
    EPFL.
    Moneta, Lorenzo
    CERN.
    Shekar, Saurav
    ETH Zurich.
    Estrade, Victor
    CERN.
    Vashistha, Akshay
    CERN. Karlsruhe Institute of Technology.
    Wunsch, Stefan
    CERN. Karlsruhe Institute of Technology.
    Mesa, Omar Andres Zapata
    University of Antioquia. Metropolitan Institute of Technology.
    New Machine Learning Developments in ROOT/TMVA2019Ingår i: 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), EDP Sciences, 2019, Vol. 214, artikel-id 06014Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation.

  • 2.
    Albertsson, Kim
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Gleyzer, Sergei
    University of Florida.
    Zapata, Omar
    OProject and University of Antioquia.
    Machine Learning in High Energy Physics Community White Paper2018Ingår i: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 1085, artikel-id 022008Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

  • 3.
    del Campo, Sergio Martin
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Albertsson, Kim
    Luleå tekniska universitet, Verksamhetsstöd, IT-Service.
    Nilsson, Joakim
    Engineering Physics student at the Luleå University of Technology.
    Eliasson, Jens
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Sandin, Fredrik
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals2013Ingår i: IEEE International Workshop on Machine Learning for Signal Processing, Piscataway, NJ: IEEE Signal Processing Society, 2013, artikel-id 6661996Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sparse signal models with learned dictionaries of morphological features provide efficient codes in a variety of applications. Such models can be useful to reduce sensor data rates and simplify the communication, processing and analysis of information, provided that the algorithm can be realized in an efficient way and that the signal allows for sparse coding. In this paper we outline an FPGA prototype of a general purpose "analog-to-feature converter", which learns an overcomplete dictionary of features from the input signal using matching pursuit and a form of Hebbian learning. The resulting code is sparse, event-based and suitable for analysis with parallel and neuromorphic processors. We present results of two case studies. The first case is a blind source separation problem where features are learned from an artificial signal with known features. We demonstrate that the learned features are qualitatively consistent with the true features. In the second case, features are learned from ball-bearing vibration data. We find that vibration signals from bearings with faults have characteristic features and codes, and that the event-based code enable a reduction of the data rate by at least one order of magnitude.

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