Gotowe bibliografie tematyczne / Systems for Machine Learning

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
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Gotowa bibliografia na temat „Systems for Machine Learning”

Autor: Grafiati
Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Systems for Machine Learning"

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Molino, Piero, i Christopher Ré. "Declarative machine learning systems". Communications of the ACM 65, nr 1 (styczeń 2022): 42–49. http://dx.doi.org/10.1145/3475167.

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Molino, Piero, i Christopher Ré. "Declarative Machine Learning Systems". Queue 19, nr 3 (30.06.2021): 46–76. http://dx.doi.org/10.1145/3475965.3479315.

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The people training and using ML models now are typically experienced developers with years of study working within large organizations, but the next wave of ML systems should allow a substantially larger number of people, potentially without any coding skills, to perform the same tasks. These new ML systems will not require users to fully understand all the details of how models are trained and used for obtaining predictions, but will provide them a more abstract interface that is less demanding and more familiar. Declarative interfaces are well-suited for this goal, by hiding complexity and favoring separation of interest, and ultimately leading to increased productivity.
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Schneier, Bruce. "Attacking Machine Learning Systems". Computer 53, nr 5 (maj 2020): 78–80. http://dx.doi.org/10.1109/mc.2020.2980761.

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Litz, Heiner, i Milad Hashemi. "Machine Learning for Systems". IEEE Micro 40, nr 5 (1.09.2020): 6–7. http://dx.doi.org/10.1109/mm.2020.3016551.

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Sidorov, Denis, Fang Liu i Yonghui Sun. "Machine Learning for Energy Systems". Energies 13, nr 18 (10.09.2020): 4708. http://dx.doi.org/10.3390/en13184708.

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.
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Et. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 2 (11.04.2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.

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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.
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Kelly, Terence. "Steampunk Machine Learning". Queue 19, nr 6 (31.12.2021): 5–17. http://dx.doi.org/10.1145/3511543.

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Fitting models to data is all the rage nowadays but has long been an essential skill of engineers. Veterans know that real-world systems foil textbook techniques by interleaving routine operating conditions with bouts of overload and failure; to be practical, a method must model the former without distortion by the latter. Surprisingly effective aid comes from an unlikely quarter: a simple and intuitive model-fitting approach that predates the Babbage Engine. The foundation of industrial-strength decision support and anomaly detection for production datacenters, this approach yields accurate yet intelligible models without hand-holding or fuss. It is easy to practice with modern analytics software and is widely applicable to computing systems and beyond.
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Ambore, Anil Kumar, T. Sri Sai Charan, U. Rohit Reddy, T. Samara Simha Reddy i Tarun G. "Flood Prediction using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 11, nr 5 (31.05.2023): 363–67. http://dx.doi.org/10.22214/ijraset.2023.51528.

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Abstract: The most frequent natural disaster in the world, flooding affects hundreds of millions of people and kills between6,000 and 18,000 people annually, with 20% of those deaths occurring in India. Several people lack access to reliable early warning systems, despite the fact that those systems already exists demonstrated may avoid an large portion of economy anddeath loss. Improved performance and cost-effective solutions are offered by this prediction system's development. In order toforecast the occurrence of floods brought on by rainfall, a prediction model is created in this article. Based on the rainfall range for certain places, the model forecasts if "flood mayhappen or not". information about rainfall in Indian districts.
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Anggi Rachmawati i Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems". International Transactions on Education Technology (ITEE) 1, nr 1 (24.11.2022): 1–9. http://dx.doi.org/10.34306/itee.v1i1.170.

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This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.
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Anggi Rachmawati i Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems". International Transactions on Education Technology (ITEE) 1, nr 1 (24.11.2022): 1–9. http://dx.doi.org/10.33050/itee.v1i1.170.

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This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.
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Rozprawy doktorskie na temat "Systems for Machine Learning"

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Shukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 48-50).
As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a rich set of literature for modeling of technical systems that implement machine learning. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of machine learning systems. This thesis provides high-level levers and frameworks to better prepare business owners to adopt machine learning to satisfy their strategic goals.
by Ritesh Shukla.
S.M. in Engineering and Management
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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms". Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.

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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.
Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
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Swere, Erick A. R. "Machine learning in embedded systems". Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/4969.

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This thesis describes novel machine learning techniques specifically designed for use in real-time embedded systems. The techniques directly address three major requirements of such learning systems. Firstly, learning must be capable of being achieved incrementally, since many applications do not have a representative training set available at the outset. Secondly, to guarantee real-time performance, the techniques must be able to operate within a deterministic and limited time bound. Thirdly, the memory requirement must be limited and known a priori to ensure the limited memory available to hold data in embedded systems will not be exceeded. The work described here has three principal contributions. The frequency table is a data structure specifically designed to reduce the memory requirements of incremental learning in embedded systems. The frequency table facilitates a compact representation of received data that is sufficient for decision tree generation. The frequency table decision tree (FTDT) learning method provides classification performance similar to existing decision tree approaches, but extends these to incremental learning while substantially reducing memory usage for practical problems. The incremental decision path (IDP) method is able to efficiently induce, from the frequency table of observations, the path through a decision tree that is necessary for the classification of a single instance. The classification performance of IDP is equivalent to that of existing decision tree algorithms, but since IDP allows the maximum number of partial decision tree nodes to be determined prior to the generation of the path, both the memory requirement and the execution time are deterministic. In this work, the viability of the techniques is demonstrated through application to realtime mobile robot navigation.
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Verleyen, Wim. "Machine learning for systems pathology". Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.

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Systems pathology attempts to introduce more holistic approaches towards pathology and attempts to integrate clinicopathological information with “-omics” technology. This doctorate researches two examples of a systems approach for pathology: (1) a personalized patient output prediction for ovarian cancer and (2) an analytical approach differentiates between individual and collective tumour invasion. During the personalized patient output prediction for ovarian cancer study, clinicopathological measurements and proteomic biomarkers are analysed with a set of newly engineered bioinformatic tools. These tools are based upon feature selection, survival analysis with Cox proportional hazards regression, and a novel Monte Carlo approach. Clinical and pathological data proves to have highly significant information content, as expected; however, molecular data has little information content alone, and is only significant when selected most-informative variables are placed in the context of the patient's clinical and pathological measures. Furthermore, classifiers based on support vector machines (SVMs) that predict one-year PFS and three-year OS with high accuracy, show how the addition of carefully selected molecular measures to clinical and pathological knowledge can enable personalized prognosis predictions. Finally, the high-performance of these classifiers are validated on an additional data set. A second study, an analytical approach differentiates between individual and collective tumour invasion, analyses a set of morphological measures. These morphological measurements are collected with a newly developed process using automated imaging analysis for data collection in combination with a Bayesian network analysis to probabilistically connect morphological variables with tumour invasion modes. Between an individual and collective invasion mode, cell-cell contact is the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelialmesenchymal transition. In conclusion, the combination of automated imaging analysis and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist. The two studies performed in this thesis illustrate the potential of a systems approach for pathology and illustrate the need of quantitative approaches in order to reveal the system behind pathology.
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Roderus, Jens, Simon Larson i Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version of Hadoop and algorithms relevant in industry machine learning, namely K-means, latent Dirichlet allocation and naive Bayes are used in the experiments. This paper provides valuable information to anyone choosing between different cluster computing frameworks. The results show everything from moderate scalability to no scalability at all. These results indicate that Hadoop as a framework may have serious restrictions in how well tasks are actually parallelized. Possible scalability improvements could be achieved by modifying the machine learning library algorithms or by Hadoop parameter tuning.
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Johansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.

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In line with technology advancements in processing power and storing capabilities through cloud services, higher demands are set on companies’ data sets. Business executives are now expecting analyses of real time data or massive data sets, where traditional Business Intelligence struggle to deliver. The interest of using machine learning to predict trends and patterns which the human eye can’t see is thus higher than ever. Time series data sets are data sets characterised by a time stamp and a value; for example, a sensor data set. The company with which I’ve been in touch collects data from sensors in a control room. In order to predict patterns and in the future using these in combination with other data, the company wants to apply machine learning on their data set. To do this effectively, the right machine learning model needs to be selected. This thesis therefore has the purpose of finding out which machine learning model, or models, from the selected platform – Azure Machine Learning Studio – works best on a time series data set with sensor data. The models are then tested through a machine learning pilot on the company’s data Throughout the thesis, multiple machine learning models from the selected platform are evaluated. For the data set in hand, the conclusion is that a supervised regression model by the type of a Decision Forest Regression model gives the best results and has the best chance to adapt to a data set growing in size. Another conclusion is that more training data is needed to give the model an even better result, especially since it’s taking date and week day into account. Adjustments of the parameters for each model might also affect the result, opening up for further improvements.
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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines". Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determination of which computer features are associated with a given performance test failure. Several key contributions are made in the course of this research including an analysis of the different types of self-healing approaches based on their contextual use, a baseline for future comparisons between self-healing frameworks that use artificial neural networks, and a successful, automated fault identification in cloud infrastructure, and more specifically virtual machines. This approach uses three established machine learning techniques: Naïve Bayes, Baum-Welch, and Contrastive Divergence Learning. The latter demonstrates minimisation of human-interaction beyond previous implementations by producing a list in decreasing order of likelihood of potential root causes (i.e. fault hypotheses) which brings the state of the art one step closer toward fully self-healing systems. This thesis also examines the impact of that different types of faults have on their respective identification. This helps to understand the validity of the data being presented, and how the field is progressing, whilst examining the differences in impact to identification between emulated thread crashes and errant user changes – a contribution believed to be unique to this research. Lastly, future research avenues and conclusions in automated fault identification are described along with lessons learned throughout this endeavor. This includes the progression of artificial neural networks, how learning algorithms are being developed and understood, and possibilities for automatically generating feature locality data.
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Michailidis, Marios. "Investigating machine learning methods in recommender systems". Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10031000/.

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This thesis investigates the use of machine learning in improving predictions of the top K* product purchases at a particular a retailer. The data used for this research is a freely-available (for research) sample of the retailer’s transactional data spanning a period of 102 weeks and consisting of several million observations. The thesis consists of four key experiments: 1. Univariate Analysis of the Dataset: The first experiment, which is the univariate analysis of the dataset, sets the background to the following chapters. It provides explanatory insight into the customers’ shopping behaviour and identifies the drivers that connect customers and products. Using various behavioural, descriptive and aggregated features, the training dataset for a group of customers is created to map their future purchasing actions for one specific week. The test dataset is then constructed to predict the purchasing actions for the forthcoming week. This constitutes a univariate analysis and the chapter is an introduction to the features included in the subsequent algorithmic processes. 2. Meta-modelling to predict top K products: The second experiment investigates the improvement in predicting the top K products in terms of precision at K (or precision@K) and Area Under Curve (AUC) through meta-modelling. It compares combining a range of common machine learning algorithms of a supervised nature within a meta-modelling framework (where each generated model will be an input to a secondary model) with any single model involved, field benchmark or simple model combination method. 3. Hybrid method to predict repeated, promotion-driven product purchases in an irregular testing environment: The third experiment demonstrates a hybrid methodology of cross validation, modelling and optimization for improving the accuracy of predicting the products the customers of a retailer will buy after havingbought them at least once with a promotional coupon. This methodology is applied in the context of a train and test environment with limited overlap - the test data includes different coupons, different customers and different time periods. Additionally this chapter uses a real life application and a stress-test of the findings in the feature engineering space from experiment 1. It also borrows ideas from ensemble (or meta) modelling as detailed in experiment 2. 4. The StackNet model: The fourth experiment proposes a framework in the form of a scalable version of [Wolpert 1992] stacked generalization being extended through cross validation methods to many levels resembling in structure a fully connected feedforward neural network where the hidden nodes represent complex functions in the form of machine learning models of any nature. The implementation of the model is made available in the Java programming language. The research contribution of this thesis is to improve the recommendation science used in the grocery and Fast Moving Consumer Goods (FMCG) markets. It seeks to identify methods of increasing the accuracy of predicting what customers are going to buy in the future by leveraging up-to-date innovations in machine learning as well as improving current processes in the areas of feature engineering, data pre-processing and ensemble modelling. For the general scientific community this thesis can be exploited to better understand the type of data available in the grocery market and to gain insights into how to structure similar machine learning and analytical projects. The extensive, computational and algorithmic framework that accompanies this thesis is also available for general use as a prototype to solve similar data challenges. References: Wolpert, D. H. (1992). Stacked generalization. Neural networks, 5(2), 241-259. Yang, X., Steck, H., Guo, Y., & Liu, Y. (2012). On top-k recommendation using social networks. In Proceedings of the sixth ACM conference on Recommender systems (pp. 67-74). ACM.
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Ilyas, Andrew. "On practical robustness of machine learning systems". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122911.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-79).
We consider the importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted perturbations to inputs which induce large change in output. We show that these adversarial examples are not only theoretical concern, by desigining the first 3D adversarial objects, and by demonstrating that these examples can be constructed even when malicious actors have little power. We suggest a potential avenue for building robust deep learning models by leveraging generative models.
by Andrew Ilyas.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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ROSA, BRUSIN ANN MARGARETH. "Machine Learning Applications to Optical Communication Systems". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2967019.

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Książki na temat "Systems for Machine Learning"

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Chen, Joy Iong-Zong, Haoxiang Wang, Ke-Lin Du i V. Suma, red. Machine Learning and Autonomous Systems. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7996-4.

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Ao, Sio-Iong, Burghard Rieger i Mahyar A. Amouzegar, red. Machine Learning and Systems Engineering. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9419-3.

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B, Rieger Burghard, Amouzegar Mahyar A i SpringerLink (Online service), red. Machine Learning and Systems Engineering. Dordrecht: Springer Science+Business Media B.V., 2011.

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Nandan Mohanty, Sachi, Vicente Garcia Diaz i G. A. E. Satish Kumar, red. Intelligent Systems and Machine Learning. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35078-8.

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Nandan Mohanty, Sachi, Vicente Garcia Diaz i G. A. E. Satish Kumar, red. Intelligent Systems and Machine Learning. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35081-8.

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Chandran, C. Karthik, M. Rajalakshmi, Sachi Nandan Mohanty i Subrata Chowdhury. Machine Learning for Healthcare Systems. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003438816.

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Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.

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Beyerer, Jürgen, Christian Kühnert i Oliver Niggemann, red. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58485-9.

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Beyerer, Jürgen, Alexander Maier i Oliver Niggemann, red. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62746-4.

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Niggemann, Oliver, i Jürgen Beyerer, red. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48838-6.

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Części książek na temat "Systems for Machine Learning"

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Zielesny, Achim. "Machine Learning". W Intelligent Systems Reference Library, 221–380. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21280-2_4.

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Grosan, Crina, i Ajith Abraham. "Machine Learning". W Intelligent Systems Reference Library, 261–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21004-4_10.

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Zielesny, Achim. "Machine Learning". W Intelligent Systems Reference Library, 229–406. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32545-3_4.

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Subramanian, Devika, i Trevor A. Cohen. "Machine Learning Systems". W Cognitive Informatics in Biomedicine and Healthcare, 135–211. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09108-7_6.

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Wehenkel, Louis A. "Machine Learning". W Automatic Learning Techniques in Power Systems, 99–144. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.

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Sotiropoulos, Dionisios N., i George A. Tsihrintzis. "Artificial Immune Systems". W Machine Learning Paradigms, 159–235. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47194-5_7.

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Hulten, Geoff. "Machine Learning Intelligence". W Building Intelligent Systems, 245–61. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_20.

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Kulkarni, Parag. "Systemic Machine Learning". W Intelligent Systems Reference Library, 49–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55312-2_3.

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Kulkarni, Parag. "Creative Machine Learning". W Intelligent Systems Reference Library, 87–118. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55312-2_5.

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Galakatos, Alex, Andrew Crotty i Tim Kraska. "Distributed Machine Learning". W Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80647-1.

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Streszczenia konferencji na temat "Systems for Machine Learning"

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Chu, Albert B., Du T. Nguyen, Alan D. Kaplan i Brian Giera. "Image classification and control of microfluidic systems". W Applications of Machine Learning, redaktorzy Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal i Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530416.

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"Machine Learning". W 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787334.

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"Machine Learning". W 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854395.

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Ivanov, Tonislav, Ayush Kumar, Denis Sharoukhov, Francis A. Ortega i Matthew Putman. "DeepFocus: A deep learning model for focusing microscope systems". W Applications of Machine Learning 2020, redaktorzy Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal i Khan M. Iftekharuddin. SPIE, 2020. http://dx.doi.org/10.1117/12.2568990.

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Axtell, Travis, Lucas A. Overbey i Lisa Woerner. "Machine learning in complex systems". W Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, redaktorzy Tien Pham, Michael A. Kolodny i Dietrich M. Wiegmann. SPIE, 2018. http://dx.doi.org/10.1117/12.2309547.

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Zhang, Jeff Jun, Kang Liu, Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman, Theocharis Theocharides, Alessandro Artussi, Muhammad Shafique i Siddharth Garg. "Building Robust Machine Learning Systems". W DAC '19: The 56th Annual Design Automation Conference 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316781.3323472.

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Martin, Hugo, Juliana Alves Pereira, Mathieu Acher i Paul Temple. "Machine Learning and Configurable Systems". W SPLC 2019: 23rd International Systems and Software Product Line Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3336294.3342383.

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Fanca, Alexandra, Adela Puscasiu, Dan-Ioan Gota i Honoriu Valean. "Recommendation Systems with Machine Learning". W 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257290.

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EL MESTARI, Soumia Zohra. "Privacy Preserving Machine Learning Systems". W AIES '22: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514094.3539530.

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Pereira, Juliana Alves, Hugo Martin, Paul Temple i Mathieu Acher. "Machine learning and configurable systems". W SPLC '20: 24th ACM International Systems and Software Product Line Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3382025.3414976.

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Raporty organizacyjne na temat "Systems for Machine Learning"

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Rouet-Leduc, Bertrand Philippe Gerard. Fault systems monitoring using machine learning. Office of Scientific and Technical Information (OSTI), wrzesień 2019. http://dx.doi.org/10.2172/1569601.

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Cary, Dakota, i Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, listopad 2020. http://dx.doi.org/10.51593/2020ca003.

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Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.
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Gordon, Diane F., i William M. Spears. Machine Learning Systems: Part 1. Concept Learning from Examples with AQ15 and Related Systems. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1991. http://dx.doi.org/10.21236/ada242472.

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Musser, Micah. Adversarial Machine Learning and Cybersecurity. Center for Security and Emerging Technology, kwiecień 2023. http://dx.doi.org/10.51593/2022ca003.

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Artificial intelligence systems are rapidly being deployed in all sectors of the economy, yet significant research has demonstrated that these systems can be vulnerable to a wide array of attacks. How different are these problems from more common cybersecurity vulnerabilities? What legal ambiguities do they create, and how can organizations ameliorate them? This report, produced in collaboration with the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center, presents the recommendations of a July 2022 workshop of experts to help answer these questions.
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Valasek, John, i Suman Chakravorty. Machine Learning Control For Highly Reconfigurable High-Order Systems. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2015. http://dx.doi.org/10.21236/ada614672.

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Stone, Peter, i Manuela Veloso. Multiagent Systems: A Survey from a Machine Learning Perspective. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1997. http://dx.doi.org/10.21236/ada333248.

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Anania, Mark, George Corbin, Matthew Kovacs, Kevin Nelson i Jeremy Tobias. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2016. http://dx.doi.org/10.21236/ad1011870.

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Nickerson, Jeffrey, Kalle Lyytinen i John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, kwiecień 2022. http://dx.doi.org/10.4271/epr2022009.

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Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement.
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Szunyogh, Istvan, Edward Ott i Brian Hunt. Machine-Learning-Assisted Hybrid Earth System Modelling. Office of Scientific and Technical Information (OSTI), kwiecień 2021. http://dx.doi.org/10.2172/1769745.

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Rudner, Tim, i Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, marzec 2021. http://dx.doi.org/10.51593/20190042.

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This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.
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