2,278 research outputs found
A Survey on Feature Selection Algorithms
One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.
DOI: 10.17762/ijritcc2321-8169.16043
Generalized Differentiable Neural Architecture Search with Performance and Stability Improvements
This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently, thereby optimizing the search process by enforcing that the networks produce similar outputs. However, the dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network, a sub-optimal proxy for the final evaluation network utilized during retraining. ICDARTS, a revised algorithm that reformulates the search phase loss functions to ensure the criteria for training the networks is consistent across both phases, is presented along with a modified process for discretizing the search network\u27s zero operations that allows the retention of these operations in the final evaluation networks. We pair the results of these changes with ablation studies of ICDARTS\u27 algorithm and network template. Multiple methods were then explored for expanding the search space of ICDARTS, including extending its operation set and implementing methods for discretizing its continuous search cells, further improving its discovered networks\u27 performance. In order to balance the flexibility of expanded search spaces with minimal compute costs, both a novel algorithm for incorporating efficient dynamic search spaces into ICDARTS and a multi-objective version of ICDARTS that incorporates an expected latency penalty term into its loss function are introduced. All enhancements to the original search algorithm are verified on two challenging scientific datasets. This work concludes by proposing and examining the preliminary results of a preliminary hierarchical version of ICDARTS that optimizes cell structures and network templates
Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial processes
in which there is no a priori graphical structure. Similar to finite element
analysis, we assign nodes of a GNN to spatial locations and use a computational
process defined on the graph to model the relationship between an initial
function defined over a space and a resulting function in the same space. We
use GNNs as a computational substrate, and show that the locations of the nodes
in space as well as their connectivity can be optimized to focus on the most
complex parts of the space. Moreover, this representational strategy allows the
learned input-output relationship to generalize over the size of the underlying
space and run the same model at different levels of precision, trading
computation for accuracy. We demonstrate this method on a traditional PDE
problem, a physical prediction problem from robotics, and learning to predict
scene images from novel viewpoints.Comment: Accepted to ICML 201
A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition
A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
Intelligent manipulation technique for multi-branch robotic systems
New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system
A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
Automated arc detection methods are needed to scan the ongoing and
next-generation wide-field imaging surveys, which are expected to contain
thousands of strong lensing systems. Arc finders are also required for a
quantitative comparison between predictions and observations of arc abundance.
Several algorithms have been proposed to this end, but machine learning methods
have remained as a relatively unexplored step in the arc finding process. In
this work we introduce a new arc finder based on pattern recognition, which
uses a set of morphological measurements derived from the Mediatrix
Filamentation Method as entries to an Artificial Neural Network (ANN). We show
a full example of the application of the arc finder, first training and
validating the ANN on simulated arcs and then applying the code on four Hubble
Space Telescope (HST) images of strong lensing systems. The simulated arcs use
simple prescriptions for the lens and the source, while mimicking HST
observational conditions. We also consider a sample of objects from HST images
with no arcs in the training of the ANN classification. We use the training and
validation process to determine a suitable set of ANN configurations, including
the combination of inputs from the Mediatrix method, so as to maximize the
completeness while keeping the false positives low. In the simulations the
method was able to achieve a completeness of about 90% with respect to the arcs
that are input to the ANN after a preselection. However, this completeness
drops to 70% on the HST images. The false detections are of the order of
3% of the objects detected in these images. The combination of Mediatrix
measurements with an ANN is a promising tool for the pattern recognition phase
of arc finding. More realistic simulations and a larger set of real systems are
needed for a better training and assessment of the efficiency of the method.Comment: Updated to match published versio
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