Some New Approaches to Machine Learning

1969 ◽  
pp. 304-324 ◽  
Author(s):  
Nicholas V. Findler
Keyword(s):  
Machine Learning ◽  
New Approaches

scholarly journals Memristors for the Curious Outsiders

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 118 ◽  
Author(s):  
Francesco Caravelli ◽  
Juan Carbajal
Keyword(s):  
Machine Learning ◽  
Physical Mechanism ◽  
Neural Computation ◽  
Dynamic Resistance ◽  
Passive Component ◽  
Internal Parameter ◽  
New Approaches ◽  
Memristive Behavior ◽  
Memristive Circuits

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.

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

2019 ◽  
Vol 21 (3) ◽  
pp. 1078-1088 ◽  
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.

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

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.

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

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.

New Approaches to Studying Rodent Behavior Using Deep Machine Learning

2021 ◽  
pp. 365-374
Author(s):  
Alexander Andreev ◽  
Eugenia Ahremenko ◽  
Danila Apushkin ◽  
Ilya Kuznetsov ◽  
Ilya Kovalenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Approaches To Studying ◽  
New Approaches ◽  
Rodent Behavior

Some New Approaches to Machine Learning

1969 ◽  
Vol 5 (3) ◽  
pp. 173-182 ◽  
Author(s):  
Nicholas Findler
Keyword(s):  
Machine Learning ◽  
New Approaches

scholarly journals Memristors for the curious outsiders

2018 ◽  
Author(s):  
Juan Pablo Carbajal ◽  
Francesco Caravelli
Keyword(s):  
Machine Learning ◽  
Physical Mechanism ◽  
Neural Computation ◽  
Dynamic Resistance ◽  
Passive Component ◽  
Internal Parameter ◽  
New Approaches ◽  
Memristive Behavior ◽  
Memristive Circuits

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.

scholarly journals The Machine Learning landscape of top taggers

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Gregor Kasieczka ◽  
Tilman Plehn ◽  
Anja Butter ◽  
Kyle Cranmer ◽  
Dipsikha Debnath ◽  
...  
Keyword(s):  
Machine Learning ◽  
Top Quarks ◽  
Network Architectures ◽  
Learning Approaches ◽  
Low Level ◽  
New Approaches ◽  
Wide Range ◽  
Modern Machine

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

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

2019 ◽  
Vol 21 (5) ◽  
pp. 2821-2821
Author(s):  
Heesoo Park ◽  
Raghvendra Mall ◽  
Fahhad H. Alharbi ◽  
Stefano Sanvito ◽  
Nouar Tabet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chem Phys ◽  
New Approaches ◽  
Phys Chem Chem Phys

Correction for ‘Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning’ by Heesoo Park et al., Phys. Chem. Chem. Phys., 2019, DOI: 10.1039/c8cp06528d.

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