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Deep learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network 5

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Deep Learning for Uneven Data in Industrial IoT Using a Dist...

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gupta, Raj Kumar Naveena, N. Rao, B. Srinivasa Rs, Rajasree Sarkar, Swagata Kumar, Kallakunta Ravi Sardar Vallabhbhai Patel College Veer Kunwar Singh University Physics Department Bihar Ara Bhabua India Kongu Engineering College Computer Technology-UG Perundurai India Gokaraju Rangaraju Institute of Engineering & Technology Department of Computer Science and Engineering Hyderabad500090 India New Horizon College of Engineering Artificial Intelligence and Machine Learning Bangalore India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Sai Leo Nagar Chennai44 India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques. © 2023 IEEE.

关键词： Generative adversarial networks

Existence of Direct Density Ratio Estimators

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arXiv 2025年

作者： Banzato, Erika Drton, Mathias Saraf-Poor, Kian Shi, Hongjian Department of Statistical Sciences University of Padova Italy TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Germany Department of Statistics Columbia University United States TUM School of Computation Information and Technology Technical University of Munich Germany

Many two-sample problems call for a comparison of two distributions from an exponential family. Density ratio estimation methods provide ways to solve such problems through direct estimation of the differences in natural parameters. The term direct indicates that one avoids estimating both marginal distributions. In this context, we consider the Kullback–Leibler Importance Estimation Procedure (KLIEP), which has been the subject of recent work on differential networks. Our main result shows that the existence of the KLIEP estimator is characterized by whether the average sufficient statistic for one sample belongs to the convex hull of the set of all sufficient statistics for data points in the second sample. For high-dimensional problems it is customary to regularize the KLIEP loss by adding the product of a tuning parameter and a norm of the vector of parameter differences. We show that the existence of the regularized KLIEP estimator requires the tuning parameter to be no less than the dual norm-based distance between the average sufficient statistic and the convex hull. The implications of these existence issues are explored in applications to differential network analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Parameter estimation

Can Multiple Phylogenetic Trees Be Displayed in a Tree-Child Network Simultaneously?

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arXiv 2022年

作者： Wu, Yufeng Zhang, Louxin Department of Computer Science and Engineering University of Connecticut Storrs CT06269 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

A binary phylogenetic network on a taxon set X is a rooted acyclic digraph in which the degree of each nonleaf node is three and its leaves (i.e. degree-one nodes) are uniquely labeled with the taxa of X. It is tree-child if each nonleaf node has at least one child of indegree one. A set of binary phylogenetic trees may or may not be simultaneously displayed in a binary tree-child network. Necessary conditions for multiple phylogenetic trees being simultaneously displayed in a tree-child network are given here. In particular, it is proved that any two phylogenetic trees can always simultaneously be displayed in some tree-child network on the same taxa set. It is also proved that any set of multiple binary phylogenetic trees can always simultaneously be displayed in some non-binary tree-child network on the same taxa set, where each nonleaf node is of either indegree one and outdegree two or indegree at least two and outdegree *** Codes 05C30 © 2022, CC BY.

关键词： Binary trees

Bayesian Inference of Transition Matrices from Incomplete Graph data with a Topological Prior

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arXiv 2022年

作者： Perri, Vincenzo Petrović, Luka V. Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is necessary to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, where weighted edges capture observations of repeated interactions between nodes, it is common to estimate the entries of such transition matrices based on the (relative) weights of edges. However in real-world settings we are often confronted with incomplete data, which turns the construction of the transition matrix based on a weighted graph into an inference problem. Moreover, we often have access to additional information, which capture topological constraints of the system, i.e. which edges in a weighted graph are (theoretically) possible and which are not. Examples include transportation networks, where we may have access to a small sample of passenger trajectories as well as the physical topology of connections, or a limited set of observed social interactions with additional information on the underlying social structure. Combining these two different sources of information to reliably infer transition matrices from incomplete data on repeated interactions is an important open challenge, with severe implications for the reliability of downstream network analysis tasks. Addressing this issue, we show that including knowledge on such topological constraints can considerably improve the inference of transition matrices, especially in situations where we only have a small number of observed interactions. To this end, we derive an analytically tractable Bayesian method that uses repeated interactions and a topological prior to perform data-efficient inference of transition matrices. We compare our approach against commonly used frequentist and Bayesian approaches both in synthetic data and in five real-world datasets, and we find that our method recovers the transition probabilities with h

关键词： Stochastic systems

A Permutation-Free Kernel Independence Test

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arXiv 2022年

作者： Shekhar, Shubhanshu Kim, Ilmun Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Yonsei University Korea Republic of Department of Applied Statistics Yonsei University Korea Republic of

In nonparametric independence testing, we observe i.i.d. data {(Xi, Yi)}n/i=1, where X ∈ X, Y ∈ Y lie in any general spaces, and we wish to test the null that X is independent of Y. Modern test statistics such as the kernel Hilbert-Schmidt Independence Criterion (HSIC) and Distance Covariance (dCov) have intractable null distributions due to the degeneracy of the underlying U-statistics. Thus, in practice, one often resorts to using permutation testing, which provides a nonasymptotic guarantee at the expense of recalculating the quadratic-time statistics (say) a few hundred times. This paper provides a simple but nontrivial modification of HSIC and dCov (called xHSIC and xdCov, pronounced "cross" HSIC/dCov) so that they have a limiting Gaussian distribution under the null, and thus do not require permutations. This requires building on the newly developed theory of cross U-statistics by Kim and Ramdas [2020], and in particular developing several nontrivial extensions of the theory in Shekhar et al. [2022], which developed an analogous permutation-free kernel two-sample test. We show that our new tests, like the originals, are consistent against fixed alternatives, and minimax rate optimal against smooth local alternatives. Numerical simulations demonstrate that compared to the full dCov or HSIC, our variants have the same power up to a √2 factor, giving practitioners a new option for large problems or data-analysis pipelines where computation, not sample size, could be the bottleneck. © 2022, CC BY.

关键词： Computation theory

Probabilistic decomposed linear dynamical systems for robust discovery of latent neural dynamics 24

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Probabilistic decomposed linear dynamical systems for robust...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yenho Chen Noga Mudrik Kyle A. Johnsen Sankaraleengam Alagapan Adam S. Charles Christopher J. Rozell Machine Learning Center Georgia Institute of Technology and Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Machine Learning Center Georgia Institute of Technology and School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9798331314385

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. Code is available at: https://***/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

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Path length bounds for gradient descent and flow

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The Journal of Machine Learning Research

The Journal of machine learning Research 2021年第1期22卷 3154-3216页

作者： Chirag Gupta Sivaraman Balakrishnan Aaditya Ramdas Machine Learning Department Carnegie Mellon University Pittsburgh PA Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA

We derive bounds on the path length ζ of gradient descent (GD) and gradient flow (GF) curves for various classes of smooth convex and nonconvex functions. Among other results, we prove that: (a) if the iterates are linearly convergent with factor (1 — c), then ζ is at most O(1/c); (b) under the Polyak-Kurdyka-Łojasiewicz (PKL) condition, ζ is at most O(√κ), where κ is the condition number, and at least Ω (√ d ∧ κ1/4); (c) for quadratics, ζ is Θ(min -√d, √log κ) and in some cases can be independent of κ; (d) assuming just convexity, ζ can be at most 24d log d; (e) for separable quasiconvex functions, ζ is Θ(√d). Thus, we advance current understanding of the properties of GD and GF curves beyond rates of convergence. We expect our techniques to facilitate future studies for other algorithms.

关键词： optimization trajectory analysis condition number self-contracted curves Polyak-Kurdyka-Łojasiewicz functions

A Review on the Applications of machine learning Algorithms for Mental Stress Detection

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A Review on the Applications of Machine Learning Algorithms ...

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Renuka Motghare Shivam Bhoyar Prateek Verma Abhay Tale Aditya Barhate Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes

关键词： Accuracy machine learning algorithms Reviews Soft sensors Human factors Mental health Transforms Speech recognition Real-time systems Strain

Network-based Neighborhood Regression

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arXiv 2024年

作者： Zhen, Yaoming Du, Jin-Hong Department of Statistical Sciences University of Toronto TorontoONM5G 1X6 Canada Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework whose regression functions depend on both the global community-level information and local connectivity structures among entities. An efficient community-wise least square optimization approach is developed to uncover the strength of regulation among the network modules while enabling asymptotic inference. With random graph theory, we derive non-asymptotic estimation error bounds for the proposed estimator, achieving exact minimax optimality. Unlike the root-n consistency typical in canonical linear regression, our model exhibits linear consistency in the number of nodes n, highlighting the advantage of incorporating neighborhood information. The effectiveness of the proposed framework is further supported by extensive numerical experiments. Application to whole-exome sequencing and RNA-sequencing Autism datasets demonstrates the usage of the proposed method in identifying the association between the gene modules of genetic variations and the gene modules of genomic differential expressions. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear regression