23,047 research outputs found
Fuzzy local linear approximation-based sequential design
When approximating complex high-fidelity black box simulators with surrogate models, the experimental design is often created sequentially. LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions. The LOLA algorithm estimates gradients to identify interesting regions, but has a bad complexity which results in long computation time when simulators are high-dimensional. In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic. Experiments show the new method is a lot faster and results in experimental designs of comparable quality
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features
Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
network (DNN) resemblance in terms of its very deep, feedforward network
architecture. The typical S-DNN aggregates a variable number of individually
learnable modules in series to assemble a DNN-alike alternative to the targeted
object recognition tasks. This work likewise devises an S-DNN instantiation,
dubbed deep analytic network (DAN), on top of the spectral histogram (SH)
features. The DAN learning principle relies on ridge regression, and some key
DNN constituents, specifically, rectified linear unit, fine-tuning, and
normalization. The DAN aptitude is scrutinized on three repositories of varying
domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10
(natural objects). The empirical results unveil that DAN escalates the SH
baseline performance over a sufficiently deep layer.Comment: 5 page
Interactive Small-Step Algorithms I: Axiomatization
In earlier work, the Abstract State Machine Thesis -- that arbitrary
algorithms are behaviorally equivalent to abstract state machines -- was
established for several classes of algorithms, including ordinary, interactive,
small-step algorithms. This was accomplished on the basis of axiomatizations of
these classes of algorithms. Here we extend the axiomatization and, in a
companion paper, the proof, to cover interactive small-step algorithms that are
not necessarily ordinary. This means that the algorithms (1) can complete a
step without necessarily waiting for replies to all queries from that step and
(2) can use not only the environment's replies but also the order in which the
replies were received
Sequential Quantum Cloning
Not all unitary operations upon a set of qubits can be implemented by
sequential interactions between each qubit and an ancillary system. We analyze
the specific case of sequential quantum cloning 1->M and prove that the minimal
dimension D of the ancilla grows linearly with the number of clones M. In
particular, we obtain D = 2M for symmetric universal quantum cloning and D =
M+1 for symmetric phase-covariant cloning. Furthermore, we provide a recipe for
the required ancilla-qubit interactions in each step of the sequential
procedure for both cases.Comment: 4 pages, no figures. New version with changes. Accepted in Physical
Review Letter
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