7,430 research outputs found
Functions of random walks on hyperplane arrangements
Many seemingly disparate Markov chains are unified when viewed as random
walks on the set of chambers of a hyperplane arrangement. These include the
Tsetlin library of theoretical computer science and various shuffling schemes.
If only selected features of the chains are of interest, then the mixing times
may change. We study the behavior of hyperplane walks, viewed on a
subarrangement of a hyperplane arrangement. These include many new examples,
for instance a random walk on the set of acyclic orientations of a graph. All
such walks can be treated in a uniform fashion, yielding diagonalizable
matrices with known eigenvalues, stationary distribution and good rates of
convergence to stationarity.Comment: Final version; Section 4 has been split into two section
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to study
the dynamics of Oja's iteration which is an online stochastic gradient descent
method for the principal component analysis. Oja's iteration maintains a
running estimate of the true principal component from streaming data and enjoys
less temporal and spatial complexities. We show that the Oja's iteration for
the top eigenvector generates a continuous-state discrete-time Markov chain
over the unit sphere. We characterize the Oja's iteration in three phases using
diffusion approximation and weak convergence tools. Our three-phase analysis
further provides a finite-sample error bound for the running estimate, which
matches the minimax information lower bound for principal component analysis
under the additional assumption of bounded samples.Comment: Appeared in NIPS 201
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Conditional Random Fields (CRFs) constitute a popular and efficient approach
for supervised sequence labelling. CRFs can cope with large description spaces
and can integrate some form of structural dependency between labels. In this
contribution, we address the issue of efficient feature selection for CRFs
based on imposing sparsity through an L1 penalty. We first show how sparsity of
the parameter set can be exploited to significantly speed up training and
labelling. We then introduce coordinate descent parameter update schemes for
CRFs with L1 regularization. We finally provide some empirical comparisons of
the proposed approach with state-of-the-art CRF training strategies. In
particular, it is shown that the proposed approach is able to take profit of
the sparsity to speed up processing and hence potentially handle larger
dimensional models
Learning Deep Structured Models
Many problems in real-world applications involve predicting several random
variables which are statistically related. Markov random fields (MRFs) are a
great mathematical tool to encode such relationships. The goal of this paper is
to combine MRFs with deep learning algorithms to estimate complex
representations while taking into account the dependencies between the output
random variables. Towards this goal, we propose a training algorithm that is
able to learn structured models jointly with deep features that form the MRF
potentials. Our approach is efficient as it blends learning and inference and
makes use of GPU acceleration. We demonstrate the effectiveness of our
algorithm in the tasks of predicting words from noisy images, as well as
multi-class classification of Flickr photographs. We show that joint learning
of the deep features and the MRF parameters results in significant performance
gains.Comment: 11 pages including referenc
Proportional fairness in wireless powered CSMA/CA based IoT networks
This paper considers the deployment of a hybrid wireless data/power access
point in an 802.11-based wireless powered IoT network. The proportionally fair
allocation of throughputs across IoT nodes is considered under the constraints
of energy neutrality and CPU capability for each device. The joint optimization
of wireless powering and data communication resources takes the CSMA/CA random
channel access features, e.g. the backoff procedure, collisions, protocol
overhead into account. Numerical results show that the optimized solution can
effectively balance individual throughput across nodes, and meanwhile
proportionally maximize the overall sum throughput under energy constraints.Comment: Accepted by Globecom 201
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