84,900 research outputs found
Simulation assisted machine learning
Motivation: In a predictive modeling setting, if sufficient details of the
system behavior are known, one can build and use a simulation for making
predictions. When sufficient system details are not known, one typically turns
to machine learning, which builds a black-box model of the system using a large
dataset of input sample features and outputs. We consider a setting which is
between these two extremes: some details of the system mechanics are known but
not enough for creating simulations that can be used to make high quality
predictions. In this context we propose using approximate simulations to build
a kernel for use in kernelized machine learning methods, such as support vector
machines. The results of multiple simulations (under various uncertainty
scenarios) are used to compute similarity measures between every pair of
samples: sample pairs are given a high similarity score if they behave
similarly under a wide range of simulation parameters. These similarity values,
rather than the original high dimensional feature data, are used to build the
kernel.
Results: We demonstrate and explore the simulation based kernel (SimKern)
concept using four synthetic complex systems--three biologically inspired
models and one network flow optimization model. We show that, when the number
of training samples is small compared to the number of features, the SimKern
approach dominates over no-prior-knowledge methods. This approach should be
applicable in all disciplines where predictive models are sought and
informative yet approximate simulations are available.
Availability: The Python SimKern software, the demonstration models (in
MATLAB, R), and the datasets are available at
https://github.com/davidcraft/SimKern.Comment: This manuscript has been accepted for publication in Bioinformatics
published by Oxford University Press:
https://doi.org/10.1093/bioinformatics/btz199 (open access). Timo M. Deist
and Andrew Patti contributed equally to this wor
Simulation-Assisted Decorrelation for Resonant Anomaly Detection
A growing number of weak- and unsupervised machine learning approaches to
anomaly detection are being proposed to significantly extend the search program
at the Large Hadron Collider and elsewhere. One of the prototypical examples
for these methods is the search for resonant new physics, where a bump hunt can
be performed in an invariant mass spectrum. A significant challenge to methods
that rely entirely on data is that they are susceptible to sculpting artificial
bumps from the dependence of the machine learning classifier on the invariant
mass. We explore two solutions to this challenge by minimally incorporating
simulation into the learning. In particular, we study the robustness of
Simulation Assisted Likelihood-free Anomaly Detection (SALAD) to correlations
between the classifier and the invariant mass. Next, we propose a new approach
that only uses the simulation for decorrelation but the Classification without
Labels (CWoLa) approach for achieving signal sensitivity. Both methods are
compared using a full background fit analysis on simulated data from the LHC
Olympics and are robust to correlations in the data.Comment: 17 pages, 7 figure
Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
A digital quantum simulation for the extended Agassi model is proposed using
a quantum platform with eight trapped ions. The extended Agassi model is an
analytically solvable model including both short range pairing and long range
monopole-monopole interactions with applications in nuclear physics and in
other many-body systems. In addition, it owns a rich phase diagram with
different phases and the corresponding phase transition surfaces. The aim of
this work is twofold: on one hand, to propose a quantum simulation of the model
at the present limits of the trapped ions facilities and, on the other hand, to
show how to use a machine learning algorithm on top of the quantum simulation
to accurately determine the phase of the system. Concerning the quantum
simulation, this proposal is scalable with polynomial resources to larger
Agassi systems. Digital quantum simulations of nuclear physics models assisted
by machine learning may enable one to outperform the fastest classical
computers in determining fundamental aspects of nuclear matter.Comment: 15 pages, 11 figures. New title and minor changes. Published in PR
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of
future communications due to its foreseen performance when applied to complex
problems. However, the application of ML to networking systems raises concerns
among network operators and other stakeholders, especially regarding
trustworthiness and reliability. In this paper, we devise the role of network
simulators for bridging the gap between ML and communications systems. In
particular, we present an architectural integration of simulators in ML-aware
networks for training, testing, and validating ML models before being applied
to the operative network. Moreover, we provide insights on the main challenges
resulting from this integration, and then give hints discussing how they can be
overcome. Finally, we illustrate the integration of network simulators into
ML-assisted communications through a proof-of-concept testbed implementation of
a residential Wi-Fi network
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