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1st International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2020

作者： Ranjit, Mercy Prasanna Ganapathy, Gopinath Manuel, Ranjit Frederick Microsoft Corporation India Ltd AI Machine Learning Bangalore India Bharathidasan University School of Computer Science and Engineering Trichy India ITC Infotech India Ltd Data Analytics AI Bangalore India

ISBN: (纸本)9781728141428

The paper compares and discusses the impact of distributed deep learning training using the Horovod framework on the performance metrics of image segmentation model trained using the Mask R-CNN (Region-based Convolutional Neural Network) architecture in comparison with the baseline metrics obtained from running the training on a single node. Image segmentation is a complex problem in comparison with image classification and the fluctuations introduced due to the distributed gradient computations while running the training distributed is less pronounced on simpler problems like image classification while it is more pronounced for image segmentation where we measure the performance at pixel level. It is important to know how the latest algorithms and distribution frameworks perform when training the model on distributed compute with parallel gradient calculations, adjusted learning rates and averaged neural network weight updates. The paper also aimed to understand if the effective batch size per training iteration while performing the training distributed did not negatively impact the ability of the model to generalize when increasing the batch size as indicated in the previous studies. The distributed training with Horovod was performed using the Azure machine learning service on scalable cloud compute cluster. The aim of the study was not comparison in-terms of the execution time which has been well established on previous studies but on understanding the impact on the quality of the predictions measured with standard validation metrics for image segmentation using the ISIC (International Skin Imaging Collaboration) 2018 Skin Lesion image dataset. © 2020 IEEE.

关键词： Image segmentation

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Uncertainty quantification for sparse Fourier recovery

arXiv

引用

arXiv 2022年

作者： Hoppe, Frederik Krahmer, Felix Verdun, Claudio Mayrink Menzel, Marion I. Rauhut, Holger Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Munich Germany Department of Mathematics Department of Electrical and Computer Engineering Technical University of Munich Munich Center for Machine Learning Munich Germany AImotion Bavaria Faculty of Electrical Engineering and Information Technology Technische Hochschule Ingolstadt Ingolstadt Department of Physics Technical University of Munich Garching and GE Healthcare Munich Germany Department of Mathematics LMU Munich Germany

One of the most prominent methods for uncertainty quantification in high-dimensional statistics is the desparsified LASSO that relies on unconstrained 1-minimization. The majority of initial works focused on real (sub-)Gaussian designs. However, in many applications, such as magnetic resonance imaging (MRI), the measurement process possesses a certain structure due to the nature of the problem. The measurement operator in MRI can be described by a subsampled Fourier matrix. The purpose of this work is to extend the uncertainty quantification process using the desparsified LASSO to design matrices originating from a bounded orthonormal system, which naturally generalizes the subsampled Fourier case and also allows for the treatment of the case where the sparsity basis is not the standard basis. In particular we construct honest confidence intervals for every pixel of an MR image that is sparse in the standard basis provided the number of measurements satisfies n Á maxts log2 s log p, s log2 pu or that is sparse with respect to the Haar wavelet basis provided a slightly larger number of measurements. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Universal inference meets random projections: a scalable test for log-concavity

arXiv

引用

arXiv 2021年

作者： Dunn, Robin Gangrade, Aditya Wasserman, Larry Ramdas, Aaditya Advanced Methodology and Data Science Novartis Pharmaceuticals Corporation Electrical Engineering and Computer Science University of Michigan Department of Electrical & Computer Engineering Boston University United States Department of Statistics & Data Science Machine Learning Department Carnegie Mellon University United States

Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated by applications across economics, survival modeling, and reliability theory. However, there do not currently exist valid tests for whether the underlying density of given data is log-concave. The recent universal inference methodology provides a valid test. The universal test relies on maximum likelihood estimation (MLE), and efficient methods already exist for finding the log-concave MLE. This yields the first test of log-concavity that is provably valid in finite samples in any dimension, for which we also establish asymptotic consistency results. Empirically, we find that a random projections approach that converts the d-dimensional testing problem into many one-dimensional problems can yield high power, leading to a simple procedure that is statistically and computationally efficient. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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QUASI-CONVERGENCE OF AN IMPLEMENTATION OF OPTIMAL BALANCE BY BACKWARD-FORWARD NUDGING

arXiv

引用

arXiv 2022年

作者： Masur, Gökce Tuba Mohamad, Haidar Oliver, Marcel Institut für Atmosphäre und Umwelt Goethe-Universität Frankfurt Frankfurt/Main60438 Germany Mathematical Institute for Machine Learning and Data Science KU Eichstätt–Ingolstadt Ingolstadt85049 Germany School of Engineering and Science Jacobs University Bremen28759 Germany

Optimal balance is a non-asymptotic numerical method for computing a point on an elliptic slow manifold for two-scale dynamical systems with strong gyroscopic forces. It works by solving a modified differential equation as a boundary value problem in time, where the nonlinear terms are adiabatically ramped up from zero to the fully nonlinear dynamics. A dedicated boundary value solver, however, is often not directly available. The most natural alternative is a nudging solver, where the problem is repeatedly solved forward and backward in time and the respective boundary conditions are restored whenever one of the temporal end points is visited. In this paper, we show quasi-convergence of this scheme in the sense that the termination residual of the nudging iteration is as small as the asymptotic error of the method itself, i.e., under appropriate assumptions exponentially small. This confirms that optimal balance in its nudging formulation is an effective algorithm. Further, it shows that the boundary value problem formulation of optimal balance is well posed up at most a residual error as small as the asymptotic error of the method itself. The key step in our proof is a careful two-component Gronwall *** Codes Primary 34E13, Secondary 34B15, 37M21 Copyright © 2022, The Authors. All rights reserved.

关键词： Boundary value problems

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Object Modeling in Kinematic Problems of Manipulation Robots

SSRN

引用

SSRN 2023年

作者： Liang, Kang Krakhmalev, Oleg Blagoveshchensky, Ivan Krakhmalev, Nikita Beiresh, Andrew Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia 4-th Veshnyakovsky Passage 4 Moscow109456 Russia Volokolamsk highway building 11 Moscow125080 Russia Department of Engineering Graphics Moscow State University of Technology "STANKIN" Vadkovsky Lane 3a Moscow127055 Russia

A method for compiling object diagrams has been developed to describe algorithms for calculating the kinematic parameters of manipulation robots. Examples of drawing up object diagrams for calculating the speeds and accelerations of points selected on the manipulation robot links are considered. Analysing the computational complexity of the obtained algorithms is carried out and a method is proposed to increase their computational efficiency. Raising the computational efficiency is achieved by reducing the addition and multiplication operations performed by the algorithm. A two-level parallel computational algorithm is developed, which makes it possible to additionally improve the computational efficiency when using multiprocessor computing systems. Information and computational graphs illustrating the developed algorithms are presented. The conducted research allows automating the process of modelling the manipulative robot movement. The object approach and the modelling methods carried out on its basis can also be implemented in the control systems for manipulation robots. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series data and Sequential Decision Making

arXiv

引用

arXiv 2023年

作者： Yu, Shujian Li, Hongming Løkse, Sigurd Jenssen, Robert Príncipe, José C. The Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway The Quantitative Data Analytics Group Vrije Universiteit Amsterdam Netherlands The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States The Drones and Autonomous Systems group at NORCE Norwegian Research Centre Tromsø Norway The Pioneer AI Centre Copenhagen University The Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional KL divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

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Adaptive hybrid density functionals

arXiv

引用

arXiv 2024年

作者： Khan, Danish Price, Alastair James Arthur Ach, Maximilian L. von Lilienfeld, O. Anatole Trottier, Olivier Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Department of Physics Ludwig-Maximilians-Universität Munich Germany Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Exact exchange and correlation contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging the exact exchange admixture and the amount of correlation over configurational and compositional degrees of freedom, hybrid density functional approximations (DFAs) have been widely successful, yet have fallen short to reach explicitly correlated high level quantum chemistry accuracy in general. Using quantum machine learning, we have adaptified hybrid DFAs by generating optimal admixture ratios of exact exchange or of correlation "on the fly", specifically for any chemical compound. Adaptive exchange in the PBE0 hybrid DFA yields atomization energies sufficiently accurate to effectively cure the infamous spin gap problem in DFT, and also improves electron densities, and HOMO-LUMO gaps in organic molecules. Using adaptive PBE0, we revised the entire QM9 data set presenting more accurate quantum properties that include, on average, stronger covalent binding, larger band-gaps, more localized electron densities, and slightly larger dipole-moments. Adaptive PBE correlation (in conjunction with 100% HF exchange) also affords much improved atomization energies, and even enables covalent bond dissociation. © 2024, CC BY.

关键词： Carrier concentration

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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

arXiv

引用

arXiv 2024年

作者： Liang, Weixin Izzo, Zachary Zhang, Yaohui Lepp, Haley Cao, Hancheng Zhao, Xuandong Chen, Lingjiao Ye, Haotian Liu, Sheng Huang, Zhi McFarland, Daniel A. Zou, James Y. Department of Computer Science Stanford University United States Machine Learning Department NEC Labs America United States Department of Electrical Engineering Stanford University United States Graduate School of Education Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Computer Science UC Santa Barbara United States Department of Biomedical Data Science Stanford University United States Department of Sociology Stanford University United States Graduate School of Business Stanford University United States

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

引用

arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression machine learning algorithms Decision making Transportation Receivers data models Classification algorithms Random forests

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