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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

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BIM: Improving Graph Neural Networks with Balanced Influence...

International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

XPASC: Measuring Generalization in Weak Supervision by Explainability and Association

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arXiv 2022年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria AI Innovation & Pre-Development Data:Lab Volkswagen AG Munich Germany UniVie Docotoral School Computer Science Vienna Austria CAPE Analytics Mountain ViewCA United States

Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of labeled data, requiring only little manual effort. Standard approaches use labeling functions to specify signals that are relevant for the labeling. It has been conjectured that weakly supervised models over-rely on those signals and as a result suffer from overfitting. To verify this assumption, we introduce a novel method, XPASC (eXPlainability-Association SCore), for measuring the generalization of a model trained with a weakly supervised dataset. Considering the occurrences of features, classes and labeling functions in a dataset, XPASC takes into account the relevance of each feature for the predictions (explainability) of the model as well as the connection of the feature with the class and the labeling function (association), respectively. The explainability is measured using occlusion in this work. The association in XPASC can be measured in two variants: XPASC-CHI SQUARE measures associations relative to their statistical significance, while XPASC-PPMI measures association strength more generally. We use XPASC to analyze KNOWMAN, an adversarial architecture intended to control the degree of generalization from the labeling functions and thus to mitigate the problem of overfitting. On one hand, we show that KNOWMAN is able to control the degree of generalization through a hyperparameter. On the other hand, results and qualitative analysis show that generalization and performance do not relate one-to-one, and that the highest degree of generalization does not necessarily imply the best performance. Therefore methods that allow for controlling the amount of generalization can achieve the right degree of benign overfitting. Our contributions in this study are i) the XPASC score to measure generalization in weakly-supervised models, ii) evaluation of XPASC across datasets and models and iii) the release of the XPASC implementation. © 2022, CC BY.

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The 28th AIAI irish conference on artificial intelligence and cognitive science

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CEUR Workshop Proceedings 2020年 2771卷

作者： Longo, Luca Rizzo, Lucas Hunter, Elizabeth Pakrashi, Arjun Technological University Dublin School of Computer Science Ireland Machine Learning Labs Science Foundation Ireland Centre for Research Training Ireland D-Real Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality Ireland CeADAR Enterprise Ireland's Centre for Applied Artificial Intelligence Ireland University College Dublin School of Computer Science Ireland Insight Centre for Data Analytics Ireland

T-Cell Receptor Optimization with Reinforcement learning and Mutation Polices for Precision Immunotherapy 27th

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T-Cell Receptor Optimization with Reinforcement Learning an...

27th International Conference on research in Computational Molecular Biology, RECOMB 2023

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada Ontario Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9783031291180

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (Formula Presented) problem, and presented a framework (Formula Presented) with a mutation policy using proximal policy optimization. (Formula Presented) mutates TCRs into effective ones that can recognize given peptides. (Formula Presented) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared (Formula Presented) with multiple baseline methods and demonstrated that (Formula Presented) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of(Formula Presented) for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

Achieving Linear Speedup with Network-Independent learning Rates in Decentralized Stochastic Optimization

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Achieving Linear Speedup with Network-Independent Learning R...

IEEE Conference on Decision and Control

作者： Hao Yuan Sulaiman A. Alghunaim Kun Yuan Center for Machine Learning Research Peking University Beijing P. R. China Dept. Electrical Engr. Kuwait University Safat Kuwait AI for Science Institute Beijing P. R. China National Engineering Labratory for Big Data Analytics and Applications Beijing P. R. China

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.

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Optimising knee injury detection with spatial attention and validating localisation ability

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arXiv 2021年

作者： Belton, Niamh Welaratne, Ivan Dahlan, Adil Hearne, Ronan T. Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin Department of Radiology Mater Misericordiae University Hospital Dublin Ireland School of Electronic Engineering University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

ICDAR 2021 competition on on-line signature verification

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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Rengifo, Santiago Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China Guangdong Artificial Intelligence and Digital Economy Laboratory China European Commission Joint Research Centre Italy Universidad del Atlantico Medio Spain Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition12, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB13 and SVC2021_EvalDB14, and standard experimental protocols. © 2021, CC BY-NC-ND.

关键词： Deep learning

: Quantum Artificial Intelligence for the Automotive Industry

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KI - Künstliche Intelligenz 2024年第4期38卷 351-359页

作者： Stollenwerk, Tobias Bhattacharya, Somtapa Cattelan, Michele Ciani, Alessandro Compostella, Gabriele Headley, David Klepsch, Johannes Klusch, Matthias Leder, Markus Macaluso, Antonio Michielsen, Kristel Nabok, Dmytro Papanikolaou, Anestis Rausch, Alexander Schumann, Marco Skolik, Andrea Yarkoni, Sheir Wilhelm, Frank K. Institute for Quantum Computing Analytics (PGI-12) Forschungszentrum Jülich Jülich Germany Volkswagen Data:Lab Volkswagen Group Munich Germany Machine Learning Research Lab Volkswagen Group Munich Germany Mercedes-Benz AG Stuttgart Germany BMW AG München Germany German Research Center for Artificial Intelligence (DFKI) Saarbrüecken Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany Robert Bosch GmbH Corporate Research Renningen Germany

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

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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