146,398 research outputs found
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
Extreme learning machine (ELM) as an emerging branch of shallow networks has
shown its excellent generalization and fast learning speed. However, for
blended data, the robustness of ELM is weak because its weights and biases of
hidden nodes are set randomly. Moreover, the noisy data exert a negative
effect. To solve this problem, a new framework called RMSE-ELM is proposed in
this paper. It is a two-layer recursive model. In the first layer, the
framework trains lots of ELMs in different groups concurrently, then employs
selective ensemble to pick out an optimal set of ELMs in each group, which can
be merged into a large group of ELMs called candidate pool. In the second
layer, selective ensemble is recursively used on candidate pool to acquire the
final ensemble. In the experiments, we apply UCI blended datasets to confirm
the robustness of our new approach in two key aspects (mean square error and
standard deviation). The space complexity of our method is increased to some
degree, but the results have shown that RMSE-ELM significantly improves
robustness with slightly computational time compared with representative
methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential
framework to solve robustness issue of ELM for high-dimensional blended data in
the future.Comment: Accepted for publication in Mathematical Problems in Engineering,
09/22/201
Robust transceiver design for MIMO relay systems with tomlinson harashima precoding
In this paper we consider a robust transceiver design for two hop non-regenerative multiple-input multiple-output (MIMO) relay networks with imperfect channel state information (CSI). The transceiver consists of Tomlinson Harashima Pre-coding (THP) at the source with a linear precoder at the relay and linear equalisation at the destination. Under the assumption that each node in the network can acquire statistical knowledge of the channel in the form of a channel mean and estimation error covariance, we optimise the processors to minimise the expected arithmetic mean square error (MSE) subject to transmission power constraints at the source and relay. Simulation results demonstrate the robustness of the proposed transceiver design to channel estimation errors
LOT: Layer-wise Orthogonal Training on Improving Certified Robustness
Recent studies show that training deep neural networks (DNNs) with Lipschitz
constraints are able to enhance adversarial robustness and other model
properties such as stability. In this paper, we propose a layer-wise orthogonal
training method (LOT) to effectively train 1-Lipschitz convolution layers via
parametrizing an orthogonal matrix with an unconstrained matrix. We then
efficiently compute the inverse square root of a convolution kernel by
transforming the input domain to the Fourier frequency domain. On the other
hand, as existing works show that semi-supervised training helps improve
empirical robustness, we aim to bridge the gap and prove that semi-supervised
learning also improves the certified robustness of Lipschitz-bounded models. We
conduct comprehensive evaluations for LOT under different settings. We show
that LOT significantly outperforms baselines regarding deterministic l2
certified robustness, and scales to deeper neural networks. Under the
supervised scenario, we improve the state-of-the-art certified robustness for
all architectures (e.g. from 59.04% to 63.50% on CIFAR-10 and from 32.57% to
34.59% on CIFAR-100 at radius rho = 36/255 for 40-layer networks). With
semi-supervised learning over unlabelled data, we are able to improve
state-of-the-art certified robustness on CIFAR-10 at rho = 108/255 from 36.04%
to 42.39%. In addition, LOT consistently outperforms baselines on different
model architectures with only 1/3 evaluation time.Comment: NeurIPS 202
Performance Measure of Hierarchical Structures for Multi-agent Systems
This paper investigates the robustness of linear consensus networks which are designed under a hierarchical scheme based on Cartesian product. For robustness analysis, consensus networks are subjected to additive white Gaussian noise. To quantify the robustness of the network, we use ℌ2-norm: the square root of the expected value of the steady state dispersion of network states. We compare several classes of undirected and directed graph topologies. We show that the hierarchical structures, designed under the Cartesian product-based hierarchy, outperform the single-layer structures in terms of robustness. We provide simulations to support the analytical results presented in this paper.acceptedVersionPeer reviewe
Robustness of correlated networks against propagating attacks
We investigate robustness of correlated networks against propagating attacks
modeled by a susceptible-infected-removed model. By Monte-Carlo simulations, we
numerically determine the first critical infection rate, above which a global
outbreak of disease occurs, and the second critical infection rate, above which
disease disintegrates the network. Our result shows that correlated networks
are robust compared to the uncorrelated ones, regardless of whether they are
assortative or disassortative, when a fraction of infected nodes in an initial
state is not too large. For large initial fraction, disassortative network
becomes fragile while assortative network holds robustness. This behavior is
related to the layered network structure inevitably generated by a rewiring
procedure we adopt to realize correlated networks.Comment: 6 pages, 13 figure
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