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Relevant bibliographies by topics / Machine Learning Model Robustness

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Academic literature on the topic 'Machine Learning Model Robustness'

Author: Grafiati

Published: 9 March 2023

Last updated: 11 March 2023

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Journal articles on the topic "Machine Learning Model Robustness"

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Arslan, Ayse. "Rethinking Robustness in Machine Learning: Use of Generative Adversarial Networks for Enhanced Robustness." Scholars Journal of Engineering and Technology 10, no. 3 (2022): 9–15. http://dx.doi.org/10.36347/sjet.2022.v10i03.001.

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Machine learning (ML) is increasingly being used in real-world applications, so understanding the uncertainty and robustness of a model is necessary to ensure performance in practice. This paper explores approximations for robustness which can meaningfully explain the behavior of any black box model. Starting with a discussion on components of a robust model this paper offers some techniques based on the Generative Adversarial Network (GAN) approach to improve the robustness of a model. The study concludes that a clear understanding of robust models for ML allows improving information for prac

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Einziger, Gil, Maayan Goldstein, Yaniv Sa’ar, and Itai Segall. "Verifying Robustness of Gradient Boosted Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2446–53. http://dx.doi.org/10.1609/aaai.v33i01.33012446.

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Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models.This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model’s robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate a capabil

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Thapa, Chandra, Pathum Chamikara Mahawaga Arachchige, Seyit Camtepe, and Lichao Sun. "SplitFed: When Federated Learning Meets Split Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8485–93. http://dx.doi.org/10.1609/aaai.v36i8.20825.

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Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learni

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Balakrishnan, Charumathi, and Mangaiyarkarasi Thiagarajan. "CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING." Buletin Ekonomi Moneter dan Perbankan 24 (March 8, 2021): 107–28. http://dx.doi.org/10.21098/bemp.v24i0.1401.

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We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

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Nguyen, Ngoc-Kim-Khanh, Quang Nguyen, Hai-Ha Pham, et al. "Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model." Complexity 2022 (November 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/3616163.

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Computing the robustness of a network, i.e., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. The Monte Carlo numerical simulation is the commonly used method to compute network robustness. However, it has a very high computational cost, especially for large networks. Here, we propose a methodology such that the robustness of large real-world social networks can be predicted using machine learning models, which are pretrained using existing datasets. We demonstrate this approach by simulating two eff

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Wu, Zhijing, and Hua Xu. "A Multi-Task Learning Machine Reading Comprehension Model for Noisy Document (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13963–64. http://dx.doi.org/10.1609/aaai.v34i10.7254.

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Current neural models for Machine Reading Comprehension (MRC) have achieved successful performance in recent years. However, the model is too fragile and lack robustness to tackle the imperceptible adversarial perturbations to the input. In this work, we propose a multi-task learning MRC model with a hierarchical knowledge enrichment to further improve the robustness for noisy document. Our model follows a typical encode-align-decode framework. Additionally, we apply a hierarchical method of adding background knowledge into the model from coarse-to-fine to enhance the language representations.

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Chuah, Joshua, Uwe Kruger, Ge Wang, Pingkun Yan, and Juergen Hahn. "Framework for Testing Robustness of Machine Learning-Based Classifiers." Journal of Personalized Medicine 12, no. 8 (2022): 1314. http://dx.doi.org/10.3390/jpm12081314.

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There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers’ performance and changes in the classifiers’ parameter values using fact

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Sepulveda, Natalia Espinoza, and Jyoti Sinha. "Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines." Machines 8, no. 4 (2020): 66. http://dx.doi.org/10.3390/machines8040066.

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Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with di

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Zhang, Lingwen, Ning Xiao, Wenkao Yang, and Jun Li. "Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization." Sensors 19, no. 1 (2019): 125. http://dx.doi.org/10.3390/s19010125.

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In the era of the Internet of Things and Artificial Intelligence, the Wi-Fi fingerprinting-based indoor positioning system (IPS) has been recognized as the most promising IPS for various applications. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods. However, currently methods are based on single-feature Received Signal Strength (RSS), which is extremely unstable in performance in terms of precision and robustness. The reason for this is that single feature machines cannot capture the complete channel characteristics and are suscepti

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Drews, Samuel, Aws Albarghouthi, and Loris D'Antoni. "Proving Data-Poisoning Robustness in Decision Trees." Communications of the ACM 66, no. 2 (2023): 105–13. http://dx.doi.org/10.1145/3576894.

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Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data poisoning , where an attacker can inject a number of malicious elements into the training set to influence the learned model. We target decision tree models, a popular and simple class of machine learning models that underlies many complex learning techniques. We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote. Antidote abstractly trains decision tre

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Dissertations / Theses on the topic "Machine Learning Model Robustness"

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Adams, William A. "Analysis of Robustness in Lane Detection using Machine Learning Models." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611.

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Lundström, Linnea. "Formally Verifying the Robustness of Machine Learning Models : A Comparative Study." Thesis, Linköpings universitet, Programvara och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167504.

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Machine learning models have become increasingly popular in recent years, and not without reason. They enable software to become more powerful, and with less human involvement. As a consequence however, the actions of the software are hard for a human to understand and anticipate. This prohibits the use of machine learning in systems where safety has to be assured, typically using formal proofs of relevant properties. This thesis is focused on robustness - one of many properties that can impact the safety of a system. There are several tools available that enable formal robustness verification

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MAURI, LARA. "DATA PARTITIONING AND COMPENSATION TECHNIQUES FOR SECURE TRAINING OF MACHINE LEARNING MODELS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/932387.

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Advances in Machine Learning (ML), coupled with increased availability of huge amounts of data collected from diverse sources and improvements in computing power, have led to a widespread adoption of ML-based solutions in critical application scenarios. However, ML models intrinsically introduce new security vulnerabilities within the systems into which they are integrated, thereby expanding their attack surface. The security of ML-based systems hinges on the robustness of the ML model employed. By interfering with any of the phases of the learning process, an adversary can manipulate data and

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Rado, Omesaad A. M. "Contributions to evaluation of machine learning models. Applicability domain of classification models." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18447.

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Artificial intelligence (AI) and machine learning (ML) present some application opportunities and challenges that can be framed as learning problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms have been successfully used in numerous classification problems. With the developing popularity of using ML models for many purposes in different domains,

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Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.

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Cette thèse de doctorat traite de l'inférence variationnelle et de la robustesse en statistique et en machine learning. Plus précisément, elle se concentre sur les propriétés statistiques des approximations variationnelles et sur la conception d'algorithmes efficaces pour les calculer de manière séquentielle, et étudie les estimateurs basés sur le Maximum Mean Discrepancy comme règles d'apprentissage qui sont robustes à la mauvaise spécification du modèle.Ces dernières années, l'inférence variationnelle a été largement étudiée du point de vue computationnel, cependant, la littérature n'a accor

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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.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-79).<br>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 pe

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Ishii, Shotaro, and David Ljunggren. "A Comparative Analysis of Robustness to Noise in Machine Learning Classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302532.

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Data that stems from real measurements often to some degree contain distortions. Such distortions are generally referred to as noise in machine learning terminology, and can lead to decreased classification accuracy and poor prediction results. In this study, three machine learning classifiers were compared by their performance and robustness in the presence of noise. More specifically, random forests, support vector machines and artificial neural networks were trained and compared on four different data sets with varying levels of noise artificially added to them. In summary, the random fores

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Ebrahimi, Javid. "Robustness of Neural Networks for Discrete Input: An Adversarial Perspective." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24535.

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In the past few years, evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Literature on adversarial examples for neural nets has largely focused on image data, which are represented as points in continuous space. However, a vast proportion of machine learning models operate on discrete input, and thus demand a similar rigor in understanding their vulnerabilities and robustness. We study robustness of neural network architectures for textual and graph inputs, through the lens of adversarial input perturbations.

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Fagogenis, Georgios. "Increasing the robustness of autonomous systems to hardware degradation using machine learning." Thesis, Heriot-Watt University, 2016. http://hdl.handle.net/10399/3378.

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Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world's state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in

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Haussamer, Nicolai Haussamer. "Model Calibration with Machine Learning." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29451.

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This dissertation focuses on the application of neural networks to financial model calibration. It provides an introduction to the mathematics of basic neural networks and training algorithms. Two simplified experiments based on the Black-Scholes and constant elasticity of variance models are used to demonstrate the potential usefulness of neural networks in calibration. In addition, the main experiment features the calibration of the Heston model using model-generated data. In the experiment, we show that the calibrated model parameters reprice a set of options to a mean relative implied vola

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Books on the topic "Machine Learning Model Robustness"

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Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75714-8.

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Subrahmanian, V. S., Chiara Pulice, James F. Brown, and Jacob Bonen-Clark. A Machine Learning Based Model of Boko Haram. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60614-5.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Springer Berlin Heidelberg, 2013.

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Widjanarko, Bambang. Pengembangan model model machine learning ketahanan pangan melalui pembentukan zona musim (ZOM) suatu wilayah: Laporan akhir hibah kompetitif penelitian sesuai prioritas nasional tahun I. Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember, 2010.

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Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology Books, 2022.

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Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology, 2022.

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Adversarial Robustness for Machine Learning. Elsevier, 2023. http://dx.doi.org/10.1016/c2020-0-01078-9.

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Machine Learning Algorithms: Adversarial Robustness in Signal Processing. Springer International Publishing AG, 2022.

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Winn, John Michael. Model-Based Machine Learning. Taylor & Francis Group, 2021.

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Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Springer, 2019.

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Book chapters on the topic "Machine Learning Model Robustness"

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Bunse, Mirko, and Katharina Morik. "Certification of Model Robustness in Active Class Selection." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_17.

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Guan, Ji, Wang Fang, and Mingsheng Ying. "Robustness Verification of Quantum Classifiers." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_7.

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AbstractSeveral important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers. However, quantum noise is a major obstacle to the practical implementation of quantum machine learning. In this work, we define a formal framework for the robustness verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. In particular, this algorithm can find adversarial examples during checking. Our approach is implemented on Google’s TensorFlow Quantum and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises, derived from the surrounding environment. The effectiveness of our robust bound and algorithm is confirmed by the experimental results, including quantum bits classification as the “Hello World” example, quantum phase recognition and cluster excitation detection from real world intractable physical problems, and the classification of MNIST from the classical world.

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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.

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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This chapter in itself might serve as a stand-alone handbook already. It contains years of experience in transferring theoretical knowledge into a practical guide.

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Mancino, Alberto Carlo Maria, and Tommaso Di Noia. "Towards Differentially Private Machine Learning Models and Their Robustness to Adversaries." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09917-5_35.

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Johnson, Patricia M., Geunu Jeong, Kerstin Hammernik, et al. "Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge." In Machine Learning for Medical Image Reconstruction. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88552-6_3.

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Lehrer, Steven F., Tian Xie, and Guanxi Yi. "Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?" In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_13.

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AbstractThis chapter first provides an illustration of the benefits of using machine learning for forecasting relative to traditional econometric strategies. We consider the short-term volatility of the Bitcoin market by realized volatility observations. Our analysis highlights the importance of accounting for nonlinearities to explain the gains of machine learning algorithms and examines the robustness of our findings to the selection of hyperparameters. This provides an illustration of how different machine learning estimators improve the development of forecast models by relaxing the functional form assumptions that are made explicit when writing up an econometric model. Our second contribution is to illustrate how deep learning can be used to measure market-level sentiment from a 10% random sample of Twitter users. This sentiment variable significantly improves forecast accuracy for every econometric estimator and machine algorithm considered in our forecasting application. This provides an illustration of the benefits of new tools from the natural language processing literature at creating variables that can improve the accuracy of forecasting models.

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Han, Bo, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, and Amaury Lendasse. "RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement." In Proceedings of ELM-2014 Volume 1. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_24.

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Conrad, F., E. Boos, M. Mälzer, H. Wiemer, and S. Ihlenfeldt. "Impact of Data Sampling on Performance and Robustness of Machine Learning Models in Production Engineering." In Lecture Notes in Production Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18318-8_47.

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Deng, Lirui, Youjian Zhao, and Heng Bao. "A Self-supervised Adversarial Learning Approach for Network Intrusion Detection System." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_5.

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AbstractThe network intrusion detection system (NIDS) plays an essential role in network security. Although many data-driven approaches from the field of machine learning have been proposed to increase the efficacy of NIDSs, it still suffers from extreme data imbalance and the performance of existing algorithms depends highly on training datasets. To counterpart the class-imbalanced problem in network intrusion detection, it is necessary for models to capture more representative clues within same categories instead of learning from only classification loss. In this paper, we proposed a self-supervised adversarial learning approach for intrusion detection, which utilize instance-level discrimination for better representation learning and employs a adversarial perturbation styled data augmentation to improve the robustness of NIDS on rarely seen attacking types. State-of-the-art result was achieved on multiple frequently-used datasets and experiment conducted on cross-dataset setting demonstrated good generalization ability.

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Labaca Castro, Raphael. "Towards Robustness." In Machine Learning under Malware Attack. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40442-0_11.

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Conference papers on the topic "Machine Learning Model Robustness"

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Zhou, Zhengbo, and Jianfei Yang. "Attentive Manifold Mixup for Model Robustness." In ICMLSC 2022: 2022 The 6th International Conference on Machine Learning and Soft Computing. ACM, 2022. http://dx.doi.org/10.1145/3523150.3523164.

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Sivaslioglu, Samed, Ferhat Ozgur Catak, and Ensar Gul. "Incrementing Adversarial Robustness with Autoencoding for Machine Learning Model Attacks." In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019. http://dx.doi.org/10.1109/siu.2019.8806432.

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Jeanselme, V., A. Wertz, G. Clermont, M. R. Pinsky, and A. Dubrawski. "Robustness of Machine Learning Models for Hemorrhage Detection." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a6320.

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Izmailov, Rauf, Sridhar Venkatesan, Achyut Reddy, Ritu Chadha, Michael De Lucia, and Alina Oprea. "Poisoning attacks on machine learning models in cyber systems and mitigation strategies." In Security, Robustness, and Trust in Artificial Intelligence and Distributed Architectures, edited by Misty Blowers, Russell D. Hall, and Venkateswara R. Dasari. SPIE, 2022. http://dx.doi.org/10.1117/12.2622112.

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Bharitkar, Sunil. "Generative Feature Models and Robustness Analysis for Multimedia Content Classification." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00025.

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Shi, Ziqiang, Chaoliang Zhong, Yasuto Yokota, Wensheng Xia, and Jun Sun. "Robustness Evaluation of Deep Learning Models Based on Local Prediction Consistency." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00224.

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Reshytko, A., D. Egorov, A. Klenitskiy, and A. Shchepetnov. "WellNet: improvement of machine learning models robustness via comprehensive multi oilfield dataset." In EAGE Subsurface Intelligence Workshop. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.2019x610116.

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Zhang, Yu-Nong, Zhen Li, Dong-Sheng Guo, Ke Chen, and Pei Chen. "Superior robustness of using power-sigmoid activation functions in Z-type models for time-varying problems solving." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890387.

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Sun, Haotian, Wenxing Zhou, and Jidong Kang. "Development of a Near-Neutral pH Stress Corrosion Cracking Growth Model for Pipelines Using Machine Learning Algorithms." In 2022 14th International Pipeline Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/ipc2022-87207.

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Abstract Near-neutral pH stress corrosion cracking (NNpHSCC) is one of the leading causes of failure for buried pipelines. Characterizing the NNpHSCC growth rate accurately remains a challenging task for the pipeline industry. In this study, an NNpHSCC growth model for buried pipelines is developed based on experimental data obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with near-neutral pH environment and subjected to cyclic internal pressures. Four machine learning algorithms, namely the random forest (RF), e

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Albeanu, Grigore, and Alexandra stefania Moloiu. "LEARNING METHODS AND TRANSFERABLE APPROACHES." In eLSE 2021. ADL Romania, 2021. http://dx.doi.org/10.12753/2066-026x-21-082.

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Learning to learn, an ability common to humans and animals, implies that the more knowledge is acquired, the better a new field can be investigated. Knowledge transfer is also a well known paradigm applied both for individuals and groups (networks). Mainly, the transfer of knowledge across individuals, groups and organizational units, is possible in our e-society, through repositories, e-learning platforms, social networks, specialized blogs, online courses etc. Self-learning, auto-didacticism, is based also on strong principles investigated by psychologists working for education. This is an o

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Reports on the topic "Machine Learning Model Robustness"

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor

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Rduner, Tim G. J., and Helen Toner. Key Concepts in AI Safety: Specification in Machine Learning. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20210031.

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This paper is the fourth 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,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learn

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Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, 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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Bajari, Patrick, Denis Nekipelov, Stephen Ryan, and Miaoyu Yang. Demand Estimation with Machine Learning and Model Combination. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w20955.

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Mueller, Juliane, Charuleka Varadharajan, Erica Siirila-Woodburn, and Charles Koven. Machine Learning for Adaptive Model Refinement to Bridge Scales. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769741.

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Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Robustness and Adversarial Examples. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20190041.

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This paper is the second 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 adversarial examples, a major challenge to robustness in modern machine learning systems.

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Hamann, Hendrik F. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1395344.

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Geza, Mangistu, T. Tesfa, Liangping Li, and M. Qiao. Toward Hybrid Physics -Machine Learning to improve Land Surface Model predictions. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769785.

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Tebaldi, Claudia, Zhangshuan Hou, Abigail Snyder, and Kalyn Dorheim. Machine Learning for a-posteriori model-observed data fusion to enhance predictive value of ESM output. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769740.

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Tang, Jinyun, William Riley, Qing Zhu, and Trevor Keenan. Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769794.

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