Relevant bibliographies by topics / Gradient learning algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gradient learning algorithm'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Gradient learning algorithm"

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Jiao, Xianqi, Jia Liu, and Zhiping Chen. "Learning Complexity of Gradient Descent and Conjugate Gradient Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17671–79. https://doi.org/10.1609/aaai.v39i17.33943.

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Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, where they are employed to minimize cost functions. In these algorithms, tunable parameters, such as step sizes or conjugate parameters, play a crucial role in determining key performance metrics, like runtime and solution quality. In this work, we introduce a framework that models algorithm selection as a statistical learning problem, and thus learning complexity can be estimated
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Dong, Xuemei, and Ding-Xuan Zhou. "Learning gradients by a gradient descent algorithm." Journal of Mathematical Analysis and Applications 341, no. 2 (2008): 1018–27. http://dx.doi.org/10.1016/j.jmaa.2007.10.044.

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Cai, Qingpeng, Ling Pan, and Pingzhong Tang. "Deterministic Value-Policy Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3316–23. http://dx.doi.org/10.1609/aaai.v34i04.5732.

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Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Ba
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Kim, Kwang In. "Robust Distributed Gradient Aggregation Using Projections onto Gradient Manifolds." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13151–59. http://dx.doi.org/10.1609/aaai.v38i12.29214.

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We study the distributed gradient aggregation problem where individual clients contribute to learning a central model by sharing parameter gradients constructed from local losses. However, errors in some gradients, caused by low-quality data or adversaries, can degrade the learning process when naively combined. Existing robust gradient aggregation approaches assume that local data represent the global data-generating distribution, which may not always apply to heterogeneous (non-i.i.d.) client data. We propose a new algorithm that can robustly aggregate gradients from potentially heterogeneou
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Li, Zhihao, Qingtao Wu, Moli Zhang, Lin Wang, Youming Ge, and Guoyong Wang. "Stochastic Zeroth-Order Multi-Gradient Algorithm for Multi-Objective Optimization." Mathematics 13, no. 4 (2025): 627. https://doi.org/10.3390/math13040627.

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Multi-objective optimization (MOO) has become an important method in machine learning, which involves solving multiple competing objective problems simultaneously. Nowadays, many MOO algorithms assume that gradient information is easily available and use this information to optimize functions. However, when encountering situations where gradients are not available, such as black-box functions or non-differentiable functions, these algorithms become ineffective. In this paper, we propose a zeroth-order MOO algorithm named SZMG (stochastic zeroth-order multi-gradient algorithm), which approximat
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Note, Johan, and Maaruf Ali. "Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms." Annals of Emerging Technologies in Computing 6, no. 3 (2022): 19–36. http://dx.doi.org/10.33166/aetic.2022.03.003.

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Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different type
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Liu, Zhipeng, Rui Feng, Xiuhan Li, Wei Wang, and Xiaoling Wu. "Gradient-Sensitive Optimization for Convolutional Neural Networks." Computational Intelligence and Neuroscience 2021 (March 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/6671830.

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Convolutional neural networks (CNNs) are effective models for image classification and recognition. Gradient descent optimization (GD) is the basic algorithm for CNN model optimization. Since GD appeared, a series of improved algorithms have been derived. Among these algorithms, adaptive moment estimation (Adam) has been widely recognized. However, local changes are ignored in Adam to some extent. In this paper, we introduce an adaptive learning rate factor based on current and recent gradients. According to this factor, we can dynamically adjust the learning rate of each independent parameter
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Iiduka, Hideaki, and Yu Kobayashi. "Training Deep Neural Networks Using Conjugate Gradient-like Methods." Electronics 9, no. 11 (2020): 1809. http://dx.doi.org/10.3390/electronics9111809.

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The goal of this article is to train deep neural networks that accelerate useful adaptive learning rate optimization algorithms such as AdaGrad, RMSProp, Adam, and AMSGrad. To reach this goal, we devise an iterative algorithm combining the existing adaptive learning rate optimization algorithms with conjugate gradient-like methods, which are useful for constrained optimization. Convergence analyses show that the proposed algorithm with a small constant learning rate approximates a stationary point of a nonconvex optimization problem in deep learning. Furthermore, it is shown that the proposed
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Zhang, Jianfei, and Zhilin Liu. "PerFreezeClip: Personalized Federated Learning Based on Adaptive Clipping." Electronics 13, no. 14 (2024): 2739. http://dx.doi.org/10.3390/electronics13142739.

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The problem of data heterogeneity is one of the main challenges facing federated learning (FL). Non-IID data usually introduce bias in the training process of FL models, which can impact the accuracy and convergence speed of the models. To this end, we propose a personalized federated learning (PFL) algorithm with adaptive dynamic adjustment of the gradient clipping boundaries and the idea of freezing to reduce the influence of non-IID data on the model, called PerFreezeClip. PerFreezeClip is a design decision regarding parameter architecture, comparing the private and federated models. PerFre
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Zhang, Baoquan, Chuyao Luo, Demin Yu, et al. "MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16687–95. http://dx.doi.org/10.1609/aaai.v38i15.29608.

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Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial intelligence. Gradient-based meta-learning effectively addresses the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (called meta-optimizer), while the inner-loop process leverages it to optimize a task-specific base learner with few examples. Although these methods have shown superior performance on FSL, the outer-loop process requires calculating second-
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Dissertations / Theses on the topic "Gradient learning algorithm"

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Holmgren, Faghihi Josef, and Paul Gorgis. "Time efficiency and mistake rates for online learning algorithms : A comparison between Online Gradient Descent and Second Order Perceptron algorithm and their performance on two different data sets." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260087.

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This dissertation investigates the differences between two different online learning algorithms: Online Gradient Descent (OGD) and Second-Order Perceptron (SOP) algorithm, and how well they perform on different data sets in terms of mistake rate, time cost and number of updates. By studying different online learning algorithms and how they perform in different environments will help understand and develop new strategies to handle further online learning tasks. The study includes two different data sets, Pima Indians Diabetes and Mushroom, together with the LIBOL library for testing. The result
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Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Discrete-time Concurrent Learning for System Identification and Applications: Leveraging Memory Usage for Good Learning." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton151298579862899.

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Heinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-108923.

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The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classifificati
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Silva, Obregón Gustavo Manuel. "Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient." Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/13903.

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Convolutional sparse representations and convolutional dictionary learning are mathematical models that consist in representing a whole signal or image as a sum of convolutions between dictionary filters and coefficient maps. Unlike the patch-based counterparts, these convolutional forms are receiving an increase attention in multiple image processing tasks, since they do not present the usual patchwise drawbacks such as redundancy, multi-evaluations and non-translational invariant. Particularly, the convolutional dictionary learning (CDL) problem is addressed as an alternating minimizati
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Aberdeen, Douglas Alexander, and doug aberdeen@anu edu au. "Policy-Gradient Algorithms for Partially Observable Markov Decision Processes." The Australian National University. Research School of Information Sciences and Engineering, 2003. http://thesis.anu.edu.au./public/adt-ANU20030410.111006.

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Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms are computationally intractable. Such computational complexity motivates approximate approaches. One such class of algorithms are the so-called policy-gradient methods from reinforcement learning. They seek to adjust the parameters of an agent in the direction that maximises the long-term average
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Sjöblom, Niklas. "Evolutionary algorithms in statistical learning : Automating the optimization procedure." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160118.

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Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process. The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning paramete
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Khirirat, Sarit. "First-Order Algorithms for Communication Efficient Distributed Learning." Licentiate thesis, KTH, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263738.

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Technological developments in devices and storages have made large volumes of data collections more accessible than ever. This transformation leads to optimization problems with massive data in both volume and dimension. In response to this trend, the popularity of optimization on high performance computing architectures has increased unprecedentedly. These scalable optimization solvers can achieve high efficiency by splitting computational loads among multiple machines. However, these methods also incur large communication overhead. To solve optimization problems with millions of parameters,
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Nguyen, Thanh Huy. "Heavy-tailed nature of stochastic gradient descent in deep learning : theoretical and empirical analysis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT003.

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Dans cette thèse, nous nous intéressons à l'algorithme du gradient stochastique (SGD). Plus précisément, nous effectuons une analyse théorique et empirique du comportement du bruit de gradient stochastique (GN), qui est défini comme la différence entre le gradient réel et le gradient stochastique, dans les réseaux de neurones profonds. Sur la base de ces résultats, nous apportons une perspective alternative aux approches existantes pour étudier SGD. Le GN dans SGD est souvent considéré comme gaussien pour des raisons mathématiques. Cette hypothèse permet d'étudier SGD comme une équation différ
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Meyer, Dominik Jakob [Verfasser], Klaus [Akademischer Betreuer] Diepold, Matthias [Gutachter] Althoff, and Klaus [Gutachter] Diepold. "Accelerated Gradient Algorithms for Robust Temporal Difference Learning / Dominik Jakob Meyer ; Gutachter: Matthias Althoff, Klaus Diepold ; Betreuer: Klaus Diepold." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1237413281/34.

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Mhanna, Elissa. "Beyond gradients : zero-order approaches to optimization and learning in multi-agent environments." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG123.

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L'essor des dispositifs connectés et des données qu'ils génèrent a stimulé le développement d'applications à grande échelle. Ces dispositifs forment des réseaux distribués avec un traitement de données décentralisé. À mesure que leur nombre augmente, des défis comme la surcharge de communication et les coûts computationnels se présentent, nécessitant des méthodes d'optimisation adaptées à des contraintes de ressources strictes, surtout lorsque les dérivées sont coûteuses ou indisponibles. Cette thèse se concentre sur les méthodes d'optimisation sans dérivées, qui sont idéales quand les dérivée
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Books on the topic "Gradient learning algorithm"

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Nonlinear performance seeking control using fuzzy model reference learning control and the method of steepest descent. National Aeronautics and Space Administration, 1997.

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Book chapters on the topic "Gradient learning algorithm"

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Gao, Jiaxin, Yao Lyu, Wenxuan Wang, Yuming Yin, Fei Ma, and Shengbo Eben Li. "Gradient Correction for Asynchronous Stochastic Gradient Descent in Reinforcement Learning." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_127.

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AbstractDistributed stochastic gradient descent techniques have gained significant attention in recent years as a prevalent approach for reinforcement learning. Current distributed learning predominantly employs synchronous or asynchronous training strategies. While the asynchronous scheme avoids idle computing resources present in synchronous methods, it grapples with the stale gradient issue. This paper introduces a novel gradient correction algorithm aimed at alleviating the stale gradient problem. By leveraging second-order information within the worker node and incorporating current param
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Cheng, Xianfu, Yanqing Yao, and Ao Liu. "An Improved Privacy-Preserving Stochastic Gradient Descent Algorithm." In Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_29.

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Walrand, Jean. "Speech Recognition: B." In Probability in Electrical Engineering and Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_12.

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AbstractOnline learning algorithms update their estimates as additional observations are made. Section 12.1 explains a simple example: online linear regression. The stochastic gradient projection algorithm is a general technique to update estimates based on additional observations; it is widely used in machine learning. Section 12.2 presents the theory behind that algorithm. When analyzing large amounts of data, one faces the problems of identifying the most relevant data and of how to use efficiently the available data. Section 12.3 explains three examples of how these questions are addressed
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Zaidi, Nayyar Abbas, David McG Squire, and David Suter. "A Gradient-Based Metric Learning Algorithm for k-NN Classifiers." In AI 2010: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_20.

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Gao, Kangkai, and Yong Wang. "A Novel Algorithm of Machine Learning: Fractional Gradient Boosting Decision Tree." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18123-8_58.

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Agathocleous, Michalis, Chris Christodoulou, Vasilis Promponas, Petros Kountouris, and Vassilis Vassiliades. "Training Bidirectional Recurrent Neural Network Architectures with the Scaled Conjugate Gradient Algorithm." In Artificial Neural Networks and Machine Learning – ICANN 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44778-0_15.

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Baniecki, Hubert, Wojciech Kretowicz, and Przemyslaw Biecek. "Fooling Partial Dependence via Data Poisoning." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.

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AbstractMany methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have t
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Ma, Yao, Tingting Zhao, Kohei Hatano, and Masashi Sugiyama. "An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44851-9_23.

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van Heeswijk, Wouter. "Smart Containers with Bidding Capacity: A Policy Gradient Algorithm for Semi-cooperative Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59747-4_4.

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Lu, Zhiwu, Qiansheng Cheng, and Jinwen Ma. "A Gradient BYY Harmony Learning Algorithm on Mixture of Experts for Curve Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11508069_33.

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Conference papers on the topic "Gradient learning algorithm"

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Bereyhi, Ali, Ben Liang, Gary Boudreau, and Ali Afana. "Novel Gradient Sparsification Algorithm via Bayesian Inference." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734719.

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Su, Haijun, Mengyuan Zhu, Xi Guo, Guoqing Li, Qian Zhang, and Tao Shen. "Federated Learning Algorithm Based on Adaptive Gradient Fusion." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744184.

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Hodlevskyi, Yurii O., and Tetiana A. Vakaliuk. "Optimal Gradient Descent Algorithm for LSTM Neural Network Learning." In 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2024. http://dx.doi.org/10.1109/sist61555.2024.10629398.

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De Villeros, Pablo, Juan Diego Sánchez-Torres, Michael Defoort, and Alexander Loukianov. "Fully Distributed Federated Learning Using a Zero-Gradient-Sum Algorithm." In 2024 21st International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2024. https://doi.org/10.1109/cce62852.2024.10770981.

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Chen, Huasong, and Yanjun Liu. "Identification of Multivariable NARX Systems Based on Conjugate Gradient Pursuit Algorithm." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065190.

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Li, Yongqi, and Xiaowei Zhang. "Adaptive moment estimation optimization algorithm using projection gradient for deep learning." In 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2025), edited by Peicheng Zhu and Guihua Lin. SPIE, 2025. https://doi.org/10.1117/12.3070740.

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Yang, Juan. "English Learning Knowledge Point Recommendation Algorithm based on Deep Deterministic Policy Gradient." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860216.

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Zhou, Tong, Shaoxue Jing, and Jinqiao Dai. "Identifying a class of output error models using a multi-gradient algorithm with optimal stacking length." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065514.

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Wei, Wenhong, Yi Meng, and Qingxia Li. "A Novel Reinforcement Learning Multi-Objective Community Detection Algorithm with $\epsilon$-Gradient-Greedy Strategy." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831561.

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Wang, Hui, Zhen-Yu Wan, Yi-Bing Hou, Ze-Gang Chen, and Tao Tao. "Differential Privacy Generative Adversarial Networks Based on Dynamic Learning Rate Constrained Adaptive Gradient Algorithm." In 2024 Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC). IEEE, 2024. https://doi.org/10.1109/csrswtc64338.2024.10811529.

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Reports on the topic "Gradient learning algorithm"

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Forteza, Nicolás, and Sandra García-Uribe. A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach. Banco de España, 2023. http://dx.doi.org/10.53479/34613.

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Errors in the collection of household finance survey data may proliferate in population estimates, especially when there is oversampling of some population groups. Manual case-by-case revision has been commonly applied in order to identify and correct potential errors and omissions such as omitted or misreported assets, income and debts. We derive a machine learning approach for the purpose of classifying survey data affected by severe errors and omissions in the revision phase. Using data from the Spanish Survey of Household Finances we provide the best-performing supervised classification al
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider on
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident
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Griffin, Andrew, Sean Griffin, Kristofer Lasko, et al. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting
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A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, 2020. http://dx.doi.org/10.4271/2020-01-5154.

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At present, with the development of Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), the decision-making for automated vehicle based on connected environment conditions has attracted more attentions. Reliability, efficiency and generalization performance are the basic requirements for the vehicle decision-making system. Therefore, this paper proposed a decision-making method for connected autonomous driving based on Wasserstein Generative Adversarial Nets-Deep Deterministic Policy Gradient (WGAIL-DDPG) algorithm. In which, the key components for reinforcement learning (RL) model
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