649 research outputs found
Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive
amounts of computational power, deep learning algorithms can learn
representations of data by exploiting multiple levels of abstraction. These
machine learning methods have greatly improved the state-of-the-art in many
challenging cognitive tasks, such as visual object recognition, speech
processing, natural language understanding and automatic translation. In
particular, one class of deep learning models, known as deep belief networks,
can discover intricate statistical structure in large data sets in a completely
unsupervised fashion, by learning a generative model of the data using
Hebbian-like learning mechanisms. Although these self-organizing systems can be
conveniently formalized within the framework of statistical mechanics, their
internal functioning remains opaque, because their emergent dynamics cannot be
solved analytically. In this article we propose to study deep belief networks
using techniques commonly employed in the study of complex networks, in order
to gain some insights into the structural and functional properties of the
computational graph resulting from the learning process.Comment: 20 pages, 9 figure
Phishing Detection using Base Classifier and Ensemble Technique
Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats
Memristive Computing
Memristive computing refers to the utilization of the memristor, the fourth
fundamental passive circuit element, in computational tasks.
The existence of the memristor was theoretically predicted in 1971 by
Leon O. Chua, but experimentally validated only in 2008 by HP Labs. A
memristor is essentially a nonvolatile nanoscale programmable resistor —
indeed, memory resistor — whose resistance, or memristance to be precise,
is changed by applying a voltage across, or current through, the device.
Memristive computing is a new area of research, and many of its fundamental
questions still remain open. For example, it is yet unclear which
applications would benefit the most from the inherent nonlinear dynamics
of memristors. In any case, these dynamics should be exploited to allow
memristors to perform computation in a natural way instead of attempting
to emulate existing technologies such as CMOS logic. Examples of such
methods of computation presented in this thesis are memristive stateful logic
operations, memristive multiplication based on the translinear principle, and
the exploitation of nonlinear dynamics to construct chaotic memristive circuits.
This thesis considers memristive computing at various levels of abstraction.
The first part of the thesis analyses the physical properties and the
current-voltage behaviour of a single device. The middle part presents memristor
programming methods, and describes microcircuits for logic and analog
operations. The final chapters discuss memristive computing in largescale
applications. In particular, cellular neural networks, and associative
memory architectures are proposed as applications that significantly benefit
from memristive implementation. The work presents several new results on
memristor modeling and programming, memristive logic, analog arithmetic
operations on memristors, and applications of memristors.
The main conclusion of this thesis is that memristive computing will
be advantageous in large-scale, highly parallel mixed-mode processing architectures.
This can be justified by the following two arguments. First,
since processing can be performed directly within memristive memory architectures,
the required circuitry, processing time, and possibly also power
consumption can be reduced compared to a conventional CMOS implementation.
Second, intrachip communication can be naturally implemented by
a memristive crossbar structure.Siirretty Doriast
Connectionist Inference Models
The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling
Deep Relational Model: A Joint Probabilistic Model with a Hierarchical Structure for Bidirectional Estimation of Image and Labels
Two different types of representations, such as an image and its manually-assigned corresponding labels, generally have complex and strong relationships to each other. In this paper, we represent such deep relationships between two different types of visible variables using an energy-based probabilistic model, called a deep relational model (DRM) to improve the prediction accuracies. A DRM stacks several layers from one visible layer on to another visible layer, sandwiching several hidden layers between them. As with restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs), all connections (weights) between two adjacent layers are undirected. During maximum likelihood (ML) -based training, the network attempts to capture the latent complex relationships between two visible variables with its deep architecture. Unlike deep neural networks (DNNs), 1) the DRM is a totally generative model and 2) allows us to generate one visible variables given the other, and 2) the parameters can be optimized in a probabilistic manner. The DRM can be also fine-tuned using DNNs, like deep belief nets (DBNs) or DBMs pre-training. This paper presents experiments conduced to evaluate the performance of a DRM in image recognition and generation tasks using the MNIST data set. In the image recognition experiments, we observed that the DRM outperformed DNNs even without fine-tuning. In the image generation experiments, we obtained much more realistic images generated from the DRM more than those from the other generative models
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