19,292 research outputs found
Maximum Resilience of Artificial Neural Networks
The deployment of Artificial Neural Networks (ANNs) in safety-critical
applications poses a number of new verification and certification challenges.
In particular, for ANN-enabled self-driving vehicles it is important to
establish properties about the resilience of ANNs to noisy or even maliciously
manipulated sensory input. We are addressing these challenges by defining
resilience properties of ANN-based classifiers as the maximal amount of input
or sensor perturbation which is still tolerated. This problem of computing
maximal perturbation bounds for ANNs is then reduced to solving mixed integer
optimization problems (MIP). A number of MIP encoding heuristics are developed
for drastically reducing MIP-solver runtimes, and using parallelization of
MIP-solvers results in an almost linear speed-up in the number (up to a certain
limit) of computing cores in our experiments. We demonstrate the effectiveness
and scalability of our approach by means of computing maximal resilience bounds
for a number of ANN benchmark sets ranging from typical image recognition
scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some
typos, rephrase the definition, and add some more existing wor
A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes
Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Networks
(ANN) by extending the concepts of understandability, correctness, and validity
that are crucial ingredients in existing certification standards. We apply the
concept in a concrete case study in designing a high-way ANN-based motion
predictor to guarantee safety properties such as impossibility for the ego
vehicle to suggest moving to the right lane if there exists another vehicle on
its right.Comment: Summary for activities conducted in the fortiss
Eigenforschungsprojekt "TdpSW - Towards dependable and predictable SW for
ML-based autonomous systems". All ANN-based motion predictors being formally
analyzed are available in the source fil
When and where do feed-forward neural networks learn localist representations?
According to parallel distributed processing (PDP) theory in psychology,
neural networks (NN) learn distributed rather than interpretable localist
representations. This view has been held so strongly that few researchers have
analysed single units to determine if this assumption is correct. However,
recent results from psychology, neuroscience and computer science have shown
the occasional existence of local codes emerging in artificial and biological
neural networks. In this paper, we undertake the first systematic survey of
when local codes emerge in a feed-forward neural network, using generated input
and output data with known qualities. We find that the number of local codes
that emerge from a NN follows a well-defined distribution across the number of
hidden layer neurons, with a peak determined by the size of input data, number
of examples presented and the sparsity of input data. Using a 1-hot output code
drastically decreases the number of local codes on the hidden layer. The number
of emergent local codes increases with the percentage of dropout applied to the
hidden layer, suggesting that the localist encoding may offer a resilience to
noisy networks. This data suggests that localist coding can emerge from
feed-forward PDP networks and suggests some of the conditions that may lead to
interpretable localist representations in the cortex. The findings highlight
how local codes should not be dismissed out of hand
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