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arXiv 2021年

作者： Schulz, Till Hendrik Welke, Pascal Wrobel, Stefan Dept. of Computer Science Universität Bonn Germany Center for Machine Learning Sankt Augustin Germany

The majority of popular graph kernels is based on the concept of Haussler’s R-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Using meaningful orders on the set of edges, which allow to construct a sequence of nested graphs, we can consider a graph at multiple granularities. For one thing, this provides access to features on different levels of resolution. Furthermore, rather than to simply compare frequencies of features in graphs, it allows for their comparison in terms of when and for how long they exist in the sequences. In this work, we propose a family of graph kernels that incorporate these existence intervals of features. While our approach can be applied to arbitrary graph features, we particularly highlight Weisfeiler-Lehman vertex labels, leading to efficient kernels. We show that using Weisfeiler-Lehman labels over certain filtrations strictly increases the expressive power over the ordinary Weisfeiler-Lehman procedure in terms of deciding graph isomorphism. In fact, this result directly yields more powerful graph kernels based on such features and has implications to graph neural networks due to their close relationship to the Weisfeiler-Lehman method. We empirically validate the expressive power of our graph kernels and show significant improvements over state-of-the-art graph kernels in terms of predictive performance on various real-world benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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A Fast Heuristic for Computing Geodesic Cores in Large Networks

arXiv

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arXiv 2022年

作者： Seiffarth, Florian Horváth, Tamás Wrobel, Stefan Dept. of Computer Science University of Bonn Bonn Germany Fraunhofer IAIS Schloß Birlinghoven Sankt Augustin Germany Fraunhofer Center for Machine Learning Schloß Birlinghoven Sankt Augustin Germany

Motivated by the increasing interest in applications of graph geodesic convexity in machine learning and data mining, we present a heuristic for computing the geodesic convex hull of node sets in networks. It generates a set of almost maximal outerplanar spanning subgraphs for the input graph, computes the geodesic closure in each of these graphs, and regards a node as an element of the convex hull if it belongs to the closed sets for at least a user specified number of outerplanar graphs. Our heuristic algorithm runs in time linear in the number of edges of the input graph, i.e., it is faster with one order of magnitude than the standard algorithm computing the closure exactly. Its performance is evaluated empirically by approximating convexity based core-periphery decomposition of networks. Our experimental results with large real-world networks show that for most networks, the proposed heuristic was able to produce close approximations significantly faster than the standard algorithm computing the exact convex hulls. For example, while our algorithm calculated an approximate core-periphery decomposition in 5 hours or less for networks with more than 20 million edges, the standard algorithm did not terminate within 50 days. Copyright © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Multiple testing under negative dependence

arXiv

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arXiv 2022年

作者： Chi, Ziyu Ramdas, Aaditya Wang, Ruodu Dept. of IEOR University of California Berkeley BerkeleyCA United States Depts. of Statistics and Machine Learning Carnegie Mellon University PittsburghPA United States Dept. of Statistics and Actuarial Science University of Waterloo WaterlooON Canada

The multiple testing literature has primarily dealt with three types of dependence assumptions between p-values: independence, positive regression dependence, and arbitrary dependence. In this paper, we provide what we believe are the first theoretical results under various notions of negative dependence (negative Gaussian dependence, negative regression dependence, negative association, negative orthant dependence and weak negative dependence). These include the Simes global null test and the Benjamini-Hochberg procedure, which are known experimentally to be anti-conservative under negative dependence. The anti-conservativeness of these procedures is bounded by factors smaller than that under arbitrary dependence (in particular, by factors independent of the number of hypotheses). We also provide new results about negatively dependent e-values, and provide several examples as to when negative dependence may arise. Our proofs are elementary and short, thus amenable to extensions. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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ClimateCast: ML-Powered Temperature Forecasting for North 24 Parganas

ClimateCast: ML-Powered Temperature Forecasting for North 24...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Abhirup Paria Anindya Ghatak Biswajit Jana Arpan Adhikary Priyanka Sen Ruma Das Dept. of Computer Sc. & Engg. (Artificial Intelligence & Machine Learning) Haldia Institute of Technology Haldia India School of Computer Sc. Engg. & Tech. Bennett University Greater Noida Uttar Pradesh Dept. of Computer Sc. & Engg. Haldia Institute of Technology Haldia India School of Applied Science & Humanities Haldia Institute of Technology Haldia India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Global warming is increasing at a rapid rate. Several efforts are made to understand its influence worldwide and in particular regions as well. Therefore, temperature prediction has become one of the most important and influential research topics because of its application in agricultural, ecological, industrial, and environmental sectors. This study reviews dif-ferent machine learning algorithms for temperature prediction and observes their superiorities. The area of study was done in North 24 Parganas, West Bengal, India. Parameters used in the dataset of the North 24 Parganas include wind speed, different types of UV radiance, cloud amount, soil moisture, precipitation rate, surface pressure, and specific and relative humidity. The relevant features are chosen from all the parameters using feature selection. The feature selection technique used is SelectKBest, using Mutual Info Regression. The different ML models used are KNN Regression, Linear Regression (LR), Decision Tree Regression, Support Vector Regression, Random Forest Regression, LightGBM Regression, XGBoost Regression, AdaBoost Regression and Ridge Regression. The experimental results were evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and R Squared (coefficient of determination). The result of this experiment revealed that XGBoost Regression has MAE, MSE, RMSE, and R Squared respectively 0.361, 0.273, 0.523, 0.989 followed by LightGBM Regression with MAE, MSE, RMSE and R squared followed by 0.366, 0.276, 0.526 and 0.988. These results show that XGBoost Regression possesses a high capability for temperature prediction

关键词： Support vector machines Temperature distribution machine learning algorithms Accuracy Wind speed Soft sensors Soil moisture Feature extraction Vectors Regression tree analysis

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Uniform-in-time Wasserstein stability bounds for (noisy) stochastic gradient descent 23

Uniform-in-time Wasserstein stability bounds for (noisy) sto...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Lingjiong Zhu Mert Gürbüzbalaban Anant Raj Umut Şimşekli Dept. of Mathematics Florida State University Dept. of Management Science and Information Systems Rutgers Business School and Center for Statistics and Machine Learning Princeton University Coordinated Science Laboratory University of Illinois Urbana-Champaign and Inria Paris CNRS Ecole Normale Supérieure PSL Research University Inria Paris CNRS Ecole Normale Supérieure PSL Research University

Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various properties of optimization algorithms, the analysis of each case typically required a different proof technique with significantly different mathematical tools. In this study, we make a novel connection between learning theory and applied probability and introduce a unified guideline for proving Wasserstein stability bounds for stochastic optimization algorithms. We illustrate our approach on stochastic gradient descent (SGD) and we obtain time-uniform stability bounds (i.e., the bound does not increase with the number of iterations) for strongly convex losses and non-convex losses with additive noise, where we recover similar results to the prior art or extend them to more general cases by using a single proof technique. Our approach is flexible and can be generalizable to other popular optimizers, as it mainly requires developing Lyapunov functions, which are often readily available in the literature. It also illustrates that ergodicity is an important component for obtaining timeuniform bounds - which might not be achieved for convex or non-convex losses unless additional noise is injected to the iterates. Finally, we slightly stretch our analysis technique and prove time-uniform bounds for SGD under convex and non-convex losses (without additional additive noise), which, to our knowledge, is novel.

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Image Copy Move Forgery Detection Using Multi-Plane Convolutional Neural Network

Image Copy Move Forgery Detection Using Multi-Plane Convolut...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： S Vagdevi Deepthi V S Shweta Dhareshwar Mohammed Al-Farouni Hanumanthakari Kalyan Rao Artificial Intelligence and Machine Learning City Engineering College Bangalore Karnataka India Computer Science and Engineering (Cyber Security) Dayananda Sagar college of engineering Bangalore Karnataka India Department of MCA Nitte Meenakshi Institute of Technology Bengaluru India The Islamic university Najaf Iraq Dept. of CSE GRIET Hyderabad Telangana India

In digital images, the digital device usage and availability of open source for image editing applications are easily manipulating the digital images. The Copy Move Forgery (CMF) is an extensive technique for duplicate or hide particular image portion without leaving visual signs. Therefore, it is complex to detect the CMF and the forensic experts are rely on efficient technique for CMF detection. The Multi Plane Convolutional Neural Network (MP-CNN) is proposed in this research for detecting CMF in images. The MICC-F220, MICC-F2000 and CASIA 2.0 datasets are used which is publicly available Kaggle dataset. This dataset is preprocessed by image denoising which reduces the noise from input image. Then, the pre-processed images are provided to MP-CNN for detecting the CMF. The metrics such as precision, accuracy, f1score and recall are applied for estimating the MP-CNN performance. The MP-CNN attains accuracy of 99.45%, 99.26% and 98.61% for MICC-F220, MICC-F2000 and CASIA 2.0 datasets when compared to existing techniques like CNN, Masked Region CNN (M-RCNN) and Contrast Limited Adaptive Histogram Equalization with CNN (CLAHE+CNN).

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Multi-View Brain Network Analysis with Cross-View Missing Network Generation

Multi-View Brain Network Analysis with Cross-View Missing Ne...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gongxu Luo Chenyang Li Hejie Cui Lichao Sun Lifang He Carl Yang Department of Machine Learning MBZUAI Abu Dhabi United Arab Emirates Dept. of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Department of Computer Science Emory University Atlanta GA United States Dept. of Computer Science and Engineering Lehigh University Bethlehem PA United States

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Parkinson’s Disease (PD), one of the most common neurological disorders, has long been a challenge in public health clinical diagnosis as well as scientific understanding. Recently, there has been an upsurge of interest in brain network analysis which benefits the understanding of brain functions and early detection of neurological disorders extensively. Multi-view brain networks with different connectivity patterns among regions of interests (ROIs) can be constructed to reflect different and complementary perspectives of the brain connectivity profile. However, the extraction of such multi-view brain networks relies on the availability of multiple neuroimaging modalities and heavy data preprocessing, which often leads to severe missing data in either view. The cross-view missing issue hinders the pragmaticality of multi-view representation learning and downstream analysis. In this work, we formulate the novel problem of cross-view brain network generation and propose CroGen, a graph generative model that can generate the missing view when only one view is given. Specifically, GroGen leverages the potential correlation between diverse views of brain networks of the same individuals. Moreover, we design a pre-train schema to make full use of the labeled individuals with only single views of brain networks. Extensive experiments on real-life Parkinson’s Progression Markers Initiative (PPMI) cohort demonstrate the supreme effectiveness of CroGen over baselines on both cross-view generation tasks and downstream PD classification.

关键词： Neurological diseases Representation learning Neuroimaging Knowledge engineering data preprocessing Network analyzers Brain modeling

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Rainfall Prediction using Novel Approach for Enhancing Crop Yield

Rainfall Prediction using Novel Approach for Enhancing Crop ...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Visweswararao Seelam Sriharsha Vikruthi Naresh Kumar Bhagavatham P V V S D Nagendrudu Goda SrinivasaRao Ayyappa Chakravarthi M. Dept of CSE PACE Institute of Technology and Sciences. Dept of CSE B V Raju Institute of Technology Telangana Narsapur Medak India Dept of CSE Vignana bharathi Institute of Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Dept of CSE KL University Vaddeswaram Guntur Andhra Pradesh Dept of CSE-Data Science KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization.

关键词： Radio frequency Training Rain Accuracy Temperature Autoencoders Linear regression Time series analysis Prediction algorithms data models

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Automated question-answer medical model based on deep learning technology 20

Automated question-answer medical model based on deep learni...

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6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Abdallah, Abdelrahman Kasem, Mahmoud Hamada, Mohamed A. Sdeek, Shaymaa MSc Machine Learning and Data Science Satbayev University Almaty Kazakhstan Information Technology Assiut University Assiut Egypt IS Department International IT University Almaty Kazakhstan Dept. Information System Assiut University Qena Egypt

ISBN: (纸本)9781450377362

Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems is the lack of answers to any given medical/health-related question. The Internet is full of forums that allow people to ask some specific questions and get great answers for them. Nevertheless, browsing these questions to locate a similar case to your own question, also finding a satisfying accurate answer is difficult and timeconsuming task. This research will introduce a solution to these problems by automating the process of generating qualified answers to these questions and creating a kind of digital doctor. Furthermore, this research will train an end-to-end model using the framework of RNN and the encoder decoder to generate sensible and useful answers to a small set of medical/health issues. The proposed model was trained and evaluated using data from various online services, such as WebMD, HealthTap, eHealthForums, and iCliniq. © 2020 ACM.

关键词： Deep learning

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CLEVA-Compass: A Continual learning Evaluation Assessment Compass to promote research transparency and comparability

arXiv

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arXiv 2021年

作者： Mundt, Martin Lang, Steven Delfosse, Quentin Kersting, Kristian AI and Machine Learning Group Dept. of Computer Science TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany

What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too narrow, resulting in several attempts at constructing guidelines in favor of specific desiderata or arguing against the validity of prevalent assumptions. In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual learning EValuation Assessment Compass, CLEVA-Compass for short. The compass provides the visual means to both identify how approaches are practically reported and how works can simultaneously be contextualized in the broader literature landscape. In addition to promoting compact specification in the spirit of recent replication trends, the CLEVA-Compass thus provides an intuitive chart to understand the priorities of individual systems, where they resemble each other, and what elements are missing towards a fair comparison. Copyright © 2021, The Authors. All rights reserved.

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