Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.

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Some New Approaches to Machine Learning

Computer Games II ◽

10.1007/978-1-4613-8754-1_23 ◽

1969 ◽

pp. 304-324 ◽

Cited By ~ 1

Author(s):

Nicholas V. Findler

Keyword(s):

Machine Learning ◽

New Approaches

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New Approaches to Surface Complexation Modeling and Application of Machine Learning to Radionuclide-Mineral Interface Reactions

10.7185/gold2021.4506 ◽

2021 ◽

Author(s):

Mavrik Zavarin ◽

Haruko Wainwright ◽

Jadallah Zouabe ◽

Elliot Chang

Keyword(s):

Machine Learning ◽

Surface Complexation ◽

Surface Complexation Modeling ◽

Interface Reactions ◽

New Approaches

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Advances in Neural Computation, Machine Learning, and Cognitive Research II

10.1007/978-3-030-01328-8 ◽

2019 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Neural Computation ◽

Cognitive Research

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Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning

Physical Chemistry Chemical Physics ◽

10.1039/c8cp06528d ◽

2019 ◽

Vol 21 (3) ◽

pp. 1078-1088 ◽

Cited By ~ 14

Author(s):

Heesoo Park ◽

Raghvendra Mall ◽

Fahhad H. Alharbi ◽

Stefano Sanvito ◽

Nouar Tabet ◽

...

Keyword(s):

Machine Learning ◽

New Approaches ◽

Halide Perovskites ◽

Light Absorbers

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers.

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Morphology and Interaction of Galaxies using Deep Learning

Proceedings of the International Astronomical Union ◽

10.1017/s1743921317000205 ◽

2016 ◽

Vol 12 (S325) ◽

pp. 205-208

Author(s):

Fernando Caro ◽

Marc Huertas-Company ◽

Guillermo Cabrera

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Visual Inspection ◽

Interaction Analysis ◽

Machine Learning Techniques ◽

Huge Amount ◽

New Approaches ◽

Learning Techniques ◽

First Galaxies ◽

The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

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Method of detecting virus-encoders in computer system using analysis of their behavior

VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MANAGEMENT COMPUTER SCIENCE AND INFORMATICS ◽

10.24143/2072-9502-2020-1-41-49 ◽

2020 ◽

Vol 2020 (1) ◽

pp. 41-49

Author(s):

Arthur Kaliev ◽

Alexandr Marenkov

Keyword(s):

Machine Learning ◽

Computer System ◽

Gradient Boosting ◽

Support Vector ◽

Model Quality ◽

Network Support ◽

New Approaches ◽

Model Training ◽

Low Efficiency ◽

A New Technique

The article considers the low efficiency of existing methods of ransomware fighting. The importance of developing new approaches to the ransomware identification in computer systems (CS) is substantiated. Heuristic analysis methods are considered as new approaches to ransomware detecting. A new technique for ransomware detecting is based on the analysis of changes in CS parameters. Using machine-learning methods there have been constructed models, which allow detecting ransomware attacks on the computer system. The aim of the experiment was to obtain a model that has the highest percentage of ransomware attacks detection and the least number of false triggering. The machine learning lgorithms used for research are the following: naive Bayes classifier, multilayer neural network, support vector machine, CatBoost gradient boosting algorithm. To build the models training datasets written in Python programming language were used. The raining datasets were collected as a result of experiments with the most popular virus-encoders. The following typical metrics were selected as key metrics for the effectiveness of machine learning models: precision, recall, F1-metric, accuracy, AUC. In the course of experiments, the values of the error matrices were formed and the main indicators of the model quality metrics were obtained. In addition to the classification efficiency metrics, the average time for performing classification operations for each of the models is given. During the process of model training and testing it was revealed that the best model for detecting ransomware is that built on the CatBoost algorithm. The conclusions were drawn about the possibility of applying the approach to detect the ransomware attacks on various computer systems.

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Might a Single Neuron Solve Interesting Machine Learning Problems through Successive Computations on Its Dendritic Tree?

Neural Computation ◽

10.1162/neco_a_01390 ◽

2021 ◽

pp. 1-18

Author(s):

Ilenna Simone Jones ◽

Konrad Paul Kording

Keyword(s):

Machine Learning ◽

Binary Tree ◽

Single Neuron ◽

Model Performance ◽

Dendritic Tree ◽

Neural Computation ◽

Learning Problems ◽

Synaptic Inputs ◽

Learning Tasks ◽

Dendritic Branches

Abstract Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how aspects of a dendritic tree, such as its branched morphology or its repetition of presynaptic inputs, determine neural computation beyond this apparent nonlinearity. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We manipulate the architecture of this model to investigate the impacts of binary branching constraints and repetition of synaptic inputs on neural computation. We find that models with such manipulations can perform well on machine learning tasks, such as Fashion MNIST or Extended MNIST. We find that model performance on these tasks is limited by binary tree branching and dendritic asymmetry and is improved by the repetition of synaptic inputs to different dendritic branches. These computational experiments further neuroscience theory on how different dendritic properties might determine neural computation of clearly defined tasks.

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