126,628 research outputs found
Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes
Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair
Adaptive sampling by information maximization
The investigation of input-output systems often requires a sophisticated
choice of test inputs to make best use of limited experimental time. Here we
present an iterative algorithm that continuously adjusts an ensemble of test
inputs online, subject to the data already acquired about the system under
study. The algorithm focuses the input ensemble by maximizing the mutual
information between input and output. We apply the algorithm to simulated
neurophysiological experiments and show that it serves to extract the ensemble
of stimuli that a given neural system ``expects'' as a result of its natural
history.Comment: 4 pages, 2 figure
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through
terrain with varying characteristics, some of which might significantly affect
the integrity of the platform. Ideally, the robot should be able to identify
areas that are safe for navigation based on its own percepts about the
environment while avoiding damage to itself. Bayesian optimisation (BO) has
been successfully applied to the task of learning a model of terrain
traversability while guiding the robot through more traversable areas. An
issue, however, is that localisation uncertainty can end up guiding the robot
to unsafe areas and distort the model being learnt. In this paper, we address
this problem and present a novel method that allows BO to consider localisation
uncertainty by applying a Gaussian process model for uncertain inputs as a
prior. We evaluate the proposed method in simulation and in experiments with a
real robot navigating over rough terrain and compare it against standard BO
methods.Comment: To appear in the proceedings of the 18th International Symposium on
Robotics Research (ISRR 2017
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