133,613 research outputs found
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical
Sciences
Group invariant machine learning by fundamental domain projections
We approach the well-studied problem of supervised group invariant and
equivariant machine learning from the point of view of geometric topology. We
propose a novel approach using a pre-processing step, which involves projecting
the input data into a geometric space which parametrises the orbits of the
symmetry group. This new data can then be the input for an arbitrary machine
learning model (neural network, random forest, support-vector machine etc).
We give an algorithm to compute the geometric projection, which is efficient
to implement, and we illustrate our approach on some example machine learning
problems (including the well-studied problem of predicting Hodge numbers of
CICY matrices), in each case finding an improvement in accuracy versus others
in the literature. The geometric topology viewpoint also allows us to give a
unified description of so-called intrinsic approaches to group equivariant
machine learning, which encompasses many other approaches in the literature.Comment: 21 pages, 4 figure
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Machine Learning Methods for Attack Detection in the Smart Grid
Attack detection problems in the smart grid are posed as statistical learning
problems for different attack scenarios in which the measurements are observed
in batch or online settings. In this approach, machine learning algorithms are
used to classify measurements as being either secure or attacked. An attack
detection framework is provided to exploit any available prior knowledge about
the system and surmount constraints arising from the sparse structure of the
problem in the proposed approach. Well-known batch and online learning
algorithms (supervised and semi-supervised) are employed with decision and
feature level fusion to model the attack detection problem. The relationships
between statistical and geometric properties of attack vectors employed in the
attack scenarios and learning algorithms are analyzed to detect unobservable
attacks using statistical learning methods. The proposed algorithms are
examined on various IEEE test systems. Experimental analyses show that machine
learning algorithms can detect attacks with performances higher than the attack
detection algorithms which employ state vector estimation methods in the
proposed attack detection framework.Comment: 14 pages, 11 Figure
Recommended from our members
Geometric Numerical Integration (hybrid meeting)
The topics of the workshop
included interactions between geometric numerical integration and numerical partial differential equations;
geometric aspects of stochastic differential equations;
interaction with optimisation and machine learning;
new applications of geometric integration in physics;
problems of discrete geometry, integrability, and algebraic aspects
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining
state-of-the-art machine learning algorithms for neural spatiotemporal signal
processing. The main goal of the library is to make temporal geometric deep
learning available for researchers and machine learning practitioners in a
unified easy-to-use framework. PyTorch Geometric Temporal was created with
foundations on existing libraries in the PyTorch eco-system, streamlined neural
network layer definitions, temporal snapshot generators for batching, and
integrated benchmark datasets. These features are illustrated with a
tutorial-like case study. Experiments demonstrate the predictive performance of
the models implemented in the library on real world problems such as
epidemiological forecasting, ridehail demand prediction and web-traffic
management. Our sensitivity analysis of runtime shows that the framework can
potentially operate on web-scale datasets with rich temporal features and
spatial structure.Comment: Source code at:
https://github.com/benedekrozemberczki/pytorch_geometric_tempora
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