21 research outputs found
Bidirectional Learning for Robust Neural Networks
A multilayer perceptron can behave as a generative classifier by applying
bidirectional learning (BL). It consists of training an undirected neural
network to map input to output and vice-versa; therefore it can produce a
classifier in one direction, and a generator in the opposite direction for the
same data. The learning process of BL tries to reproduce the neuroplasticity
stated in Hebbian theory using only backward propagation of errors. In this
paper, two novel learning techniques are introduced which use BL for improving
robustness to white noise static and adversarial examples. The first method is
bidirectional propagation of errors, which the error propagation occurs in
backward and forward directions. Motivated by the fact that its generative
model receives as input a constant vector per class, we introduce as a second
method the hybrid adversarial networks (HAN). Its generative model receives a
random vector as input and its training is based on generative adversarial
networks (GAN). To assess the performance of BL, we perform experiments using
several architectures with fully and convolutional layers, with and without
bias. Experimental results show that both methods improve robustness to white
noise static and adversarial examples, and even increase accuracy, but have
different behavior depending on the architecture and task, being more
beneficial to use the one or the other. Nevertheless, HAN using a convolutional
architecture with batch normalization presents outstanding robustness, reaching
state-of-the-art accuracy on adversarial examples of hand-written digits.Comment: 8 pages, 4 figures, submitted to 2019 International Joint Conference
on Neural Network
Assessing the robustness of critical behavior in stochastic cellular automata
There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able to remain in it under perturbations. In this work, we address the question of robustness of critical systems to noise. In particular, we investigate the robustness of stochastic cellular automata (CAs) at criticality. A stochastic CA is one of the simplest stochastic models showing criticality. The transition state of stochastic CA is defined through a set of probabilities. We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise. We present the results using error metrics of the resulting power-law fitting, such as Kolmogorov–Smirnov statistic and Kullback–Leibler divergence. We discuss the implication of our results in regards to future realization of brain-inspired artificial intelligence systems.publishedVersio
Collective control of modular soft robots via embodied Spiking Neural Cellular Automata
Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of
several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple
agents, namely the voxels, which must cooperate to give rise to the overall VSR
behavior. Within this paradigm, collective intelligence plays a key role in
enabling the emerge of coordination, as each voxel is independently controlled,
exploiting only the local sensory information together with some knowledge
passed from its direct neighbors (distributed or collective control). In this
work, we propose a novel form of collective control, influenced by Neural
Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks:
the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA,
and find them to be competitive with the state-of-the-art distributed
controllers for the task of locomotion. In addition, our findings show
significant improvement with respect to the baseline in terms of adaptability
to unforeseen environmental changes, which could be a determining factor for
physical practicability of VSRs.Comment: Workshop on "From Cells to Societies: Collective Learning across
Scales" at the International Conference on Learning Representations
(Cells2Societies@ICLR
A general representation of dynamical systems for reservoir computing
Dynamical systems are capable of performing computation in a reservoir
computing paradigm. This paper presents a general representation of these
systems as an artificial neural network (ANN). Initially, we implement the
simplest dynamical system, a cellular automaton. The mathematical fundamentals
behind an ANN are maintained, but the weights of the connections and the
activation function are adjusted to work as an update rule in the context of
cellular automata. The advantages of such implementation are its usage on
specialized and optimized deep learning libraries, the capabilities to
generalize it to other types of networks and the possibility to evolve cellular
automata and other dynamical systems in terms of connectivity, update and
learning rules. Our implementation of cellular automata constitutes an initial
step towards a general framework for dynamical systems. It aims to evolve such
systems to optimize their usage in reservoir computing and to model physical
computing substrates.Comment: 5 pages, 3 figures, accepted workshop paper at Workshop on Novel
Substrates and Models for the Emergence of Developmental, Learning and
Cognitive Capabilities at IEEE ICDL-EPIROB 201
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In
the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost