248,173 research outputs found
Combined local search strategy for learning in networks of binary synapses
Learning in networks of binary synapses is known to be an NP-complete
problem. A combined stochastic local search strategy in the synaptic weight
space is constructed to further improve the learning performance of a single
random walker. We apply two correlated random walkers guided by their Hamming
distance and associated energy costs (the number of unlearned patterns) to
learn a same large set of patterns. Each walker first learns a small part of
the whole pattern set (partially different for both walkers but with the same
amount of patterns) and then both walkers explore their respective weight
spaces cooperatively to find a solution to classify the whole pattern set
correctly. The desired solutions locate at the common parts of weight spaces
explored by these two walkers. The efficiency of this combined strategy is
supported by our extensive numerical simulations and the typical Hamming
distance as well as energy cost is estimated by an annealed computation.Comment: 7 pages, 4 figures, figures and references adde
Bump formation in a binary attractor neural network
This paper investigates the conditions for the formation of local bumps in
the activity of binary attractor neural networks with spatially dependent
connectivity. We show that these formations are observed when asymmetry between
the activity during the retrieval and learning is imposed. Analytical
approximation for the order parameters is derived. The corresponding phase
diagram shows a relatively large and stable region, where this effect is
observed, although the critical storage and the information capacities
drastically decrease inside that region. We demonstrate that the stability of
the network, when starting from the bump formation, is larger than the
stability when starting even from the whole pattern. Finally, we show a very
good agreement between the analytical results and the simulations performed for
different topologies of the network.Comment: about 14 page
The effect of Hebbian plasticity on the attractors of a dynamical system
Poster presentation A central problem in neuroscience is to bridge local synaptic plasticity and the global behavior of a system. It has been shown that Hebbian learning of connections in a feedforward network performs PCA on its inputs [1]. In recurrent Hopfield network with binary units, the Hebbian-learnt patterns form the attractors of the network [2]. Starting from a random recurrent network, Hebbian learning reduces system complexity from chaotic to fixed point [3]. In this paper, we investigate the effect of Hebbian plasticity on the attractors of a continuous dynamical system. In a Hopfield network with binary units, it can be shown that Hebbian learning of an attractor stabilizes it with deepened energy landscape and larger basin of attraction. We are interested in how these properties carry over to continuous dynamical systems. Consider system of the form Math(1) where xi is a real variable, and fi a nondecreasing nonlinear function with range [-1,1]. T is the synaptic matrix, which is assumed to have been learned from orthogonal binary ({1,-1}) patterns ξμ, by the Hebbian rule: Math. Similar to the continuous Hopfield network [4], ξμ are no longer attractors, unless the gains gi are big. Assume that the system settles down to an attractor X*, and undergoes Hebbian plasticity: T´ = T + εX*X*T, where ε > 0 is the learning rate. We study how the attractor dynamics change following this plasticity. We show that, in system (1) under certain general conditions, Hebbian plasticity makes the attractor move towards its corner of the hypercube. Linear stability analysis around the attractor shows that the maximum eigenvalue becomes more negative with learning, indicating a deeper landscape. This in a way improves the system´s ability to retrieve the corresponding stored binary pattern, although the attractor itself is no longer stabilized the way it does in binary Hopfield networks
An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks
The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio
Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-time
Identifikasi identitas individu melalui pengenalan wajah secara otomatis merupakan suatu persoalan besar yang menarik dan banyak sekali berbagai macam pendekatan untuk menyelesaikan persoalan ini. Apalagi di dalam skenario kehidupan nyata yang tidak terkontrol, wajah akan terlihat dari berbagai sisi dan tidak selalu menghadap ke depan yang membuat permasalahan klasifikasi menjadi lebih sulit diselesaikan. Dalam Tugas Akhir ini digunakan salah satu metode deep neural networks yaitu Convolutional Neural Networks (CNN) sebagai pengenalan wajah secara real-time yang sudah terbukti sangat efisien dalam klasifikasi wajah. Metode diimplementasikan dengan bantuan library OpenCV untuk deteksi multi wajah dan perangkat Web Cam M-Tech 5MP. Dalam penyusunan arsitekur model Convolutional Neural Networks dilakukan konfigurasi inisialisasi parameter untuk mempercepat proses training jaringan. Hasil uji coba dengan munggunakan konstruksi model Convolutional Neural Networks sampai kedalaman 7 lapisan dengan input dari hasil ekstraksi Extended Local Binary Pattern dengan radius 1 dan neighbor 15 menunjukkan kinerja pengenalan wajah meraih rata-rata tingkat akurasi lebih dari 89% dalam ∓ 2 frame per detik
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