272 research outputs found
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
In this paper, we investigate whether is it possible to train a neural
network directly from user inputs. We consider this approach to be highly
relevant for applications in which the point of optimality is not well-defined
and user-dependent. Our application is medical image denoising which is
essential in fluoroscopy imaging. In this field every user, i.e. physician, has
a different flavor and image quality needs to be tailored towards each
individual.
To address this important problem, we propose to construct a loss function
derived from a forced-choice experiment. In order to make the learning problem
feasible, we operate in the domain of precision learning, i.e., we inspire the
network architecture by traditional signal processing methods in order to
reduce the number of trainable parameters. The algorithm that was used for this
is a Laplacian pyramid with only six trainable parameters.
In the experimental results, we demonstrate that two image experts who prefer
different filter characteristics between sharpness and de-noising can be
created using our approach. Also models trained for a specific user perform
best on this users test data. This approach opens the way towards
implementation of direct user feedback in deep learning and is applicable for a
wide range of application.Comment: Accepted on BVM 2019; Extended ArXiv Version with additional figures
and detail
A Comparison of the Use of Binary Decision Trees and Neural Networks in Top Quark Detection
The use of neural networks for signal vs.~background discrimination in
high-energy physics experiment has been investigated and has compared favorably
with the efficiency of traditional kinematic cuts. Recent work in top quark
identification produced a neural network that, for a given top quark mass,
yielded a higher signal to background ratio in Monte Carlo simulation than a
corresponding set of conventional cuts. In this article we discuss another
pattern-recognition algorithm, the binary decision tree. We have applied a
binary decision tree to top quark identification at the Tevatron and found it
to be comparable in performance to the neural network. Furthermore,
reservations about the "black box" nature of neural network discriminators do
not apply to binary decision trees; a binary decision tree may be reduced to a
set of kinematic cuts subject to conventional error analysis.Comment: 14pp. Plain TeX + mtexsis.tex (latter available through 'get
mtexsis.tex'.) Two postscript files avail. by emai
Forecasting workload and airspace configuration with neural networks and tree search methods
International audienceThe aim of the research presented in this paper is to forecast air traffic controller workload and required airspace configuration changes with enough lead time and with a good degree of realism. For this purpose, tree search methods were combined with a neural network. The neural network takes relevant air traffic complexity metrics as input and provides a workload indication (high, normal, or low) for any given air traffic control (ATC) sector. It was trained on historical data, i.e. archived sector operations, considering that ATC sectors made up of several airspace modules are usually split into several smaller sectors when the workload is excessive, or merged with other sectors when the workload is low. The input metrics are computed from the sector geometry and from simulated or real aircraft trajectories. The tree search methods explore all possible combinations of elementary airspace modules in order to build an optimal airspace partition where the workload is balanced as well as possible across the ATC sectors. The results are compared both to the real airspace configurations and to the forecast made by flow management operators in a French "en-route" air traffic control centre
The role of biases in on-line learning of two-layer networks
The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase which has often been predominant in the original model all but disappears for a non-degenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g. attractive suboptimal symmetric phases even for realizable cases and noiseless data
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