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Robust Hyperbolic learning with Curvature-Aware Optimization

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arXiv 2024年

作者： Bdeir, Ahmad Burchert, Johannes Schmidt-Thieme, Lars Landwehr, Niels Department of Data Analytics University of Hildesheim Germany The Information Systems and Machine Learning Lab University of Hildesheim Germany

Hyperbolic deep learning has become a growing research direction in computer vision due to the unique properties afforded by the alternate embedding space. The negative curvature and exponentially growing distance metric provide a natural framework for capturing hierarchical relationships between datapoints and allowing for finer separability between their embeddings. However, current hyperbolic learning approaches are still prone to overfitting, computationally expensive, and prone to instability, especially when attempting to learn the manifold curvature to adapt to tasks and different datasets. To address these issues, our paper presents a derivation for Riemannian AdamW that helps increase hyperbolic generalization ability. For improved stability, we introduce a novel fine-tunable hyperbolic scaling approach to constrain hyperbolic embeddings and reduce approximation errors. Using this along with our curvature-aware learning schema for Lorentzian Optimizers enables the combination of curvature and non-trivialized hyperbolic parameter learning. Our approach demonstrates consistent performance improvements across Computer Vision, EEG classification, and hierarchical metric learning tasks achieving state-of-the-art results in two domains and drastically reducing runtime. © 2024, CC BY.

关键词： Adversarial machine learning

OculusGraphy: Description and Time Domain Analysis of Full-Field Electroretinograms database

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OculusGraphy: Description and Time Domain Analysis of Full-F...

2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

作者： Albasu, Faisal B. Dey, Subhankar Dolganov, Anton Yu. Hamzaoui, Oussama El Mustafa, Wisam M. Zhdanov, Aleksei E. Eng. Sch. of Info. Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia University of Erlangen-Nuremberg Machine Learning and Data Analytics Lab Erlangen Germany

ISBN: (纸本)9798350336054

Electroretinography is an established electrophysiological technique for clinical investigation of visual health function. The test involves the non-invasive assessment of the retinal function using light stimulation. This study presents a comprehensive database of five types of signals, including Scotopic 2.0 ERG response, Photopic 2.0 ERG response, Maximum 2.0 ERG response, Photopic 2.0 EGR Flicker response and Scotopic 2.0 ERG Oscillatory Potentials. The database includes 1975 signals from 323 patients, consisting of both adults and children. This study seeks to evaluate and analyze the classical parameters of three of the five signals, specifically Scotopic 2.0 ERG response, Photopic 2.0 ERG response, and Maximum 2.0 ERG response, using time-domain analysis. Correlation analysis was conducted in order to gain insights into the potential relationship between a patient's age and the classical parameters. However, the results suggest little to no correlation between these factors. The database presented in this study will serve as a useful resource for further research in the field of electrophysiology. Future research in this area could explore the use of other advanced analysis methods, such as frequency and time-frequency domain analyses, in order to gain further insights into the nuances of these signals and to better differentiate between healthy patients and those with pathologies. Additionally, further research is needed to establish norms for pediatric signals in order to better understand retinal health in children. © 2023 IEEE.

关键词： database systems

Equivariant Representation learning for Augmentation-based Self-Supervised learning via Image Reconstruction

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arXiv 2024年

作者： Wang, Qin Krajsek, Kai Scharr, Hanno IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Germany Forschungszentrum Jülich Germany

Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but often neglecting equivariant ones. This limitation reduces the generalizability of foundation models, particularly for downstream tasks requiring equivariance. We propose integrating an image reconstruction task as an auxiliary component in augmentation-based self-supervised learning algorithms to facilitate equivariant feature learning without additional parameters. Our method implements a cross-attention mechanism to blend features learned from two augmented views, subsequently reconstructing one of them. This approach is adaptable to various datasets and augmented-pair based learning methods. We evaluate its effectiveness on learning equivariant features through multiple linear regression tasks and downstream applications on both artificial (3DIEBench) and natural (ImageNet) datasets. Results consistently demonstrate significant improvements over standard augmentation-based self-supervised learning methods and state-of-the-art approaches, particularly excelling in scenarios involving combined augmentations. Our method enhances the learning of both invariant and equivariant features, leading to more robust and generalizable visual representations for computer vision tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

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LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Bridging Gaps with Generative AI: Enhancing Hypertension Monitoring Through Patient and Provider Insights

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Bridging Gaps with Generative AI: Enhancing Hypertension Mon...

34th Medical Informatics Europe Conference, MIE 2024

作者： Andreadis, Katerina Rodriguez, Danissa V. Zakreuskaya, Anastasiya Chen, Ji Gonzalez, Javier Mann, Devin New York University Grossman School of Medicine New York United States Machine Learning and Data Analytics Lab Univerity Erlangen-Nuremberg Germany MCIT Department of Health Informatics New York University Langone Health New York United States

ISBN: (纸本)9781643685335

This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key challenges in Remote Patient Monitoring (RPM) for hypertension. After a comprehensive needs assessment from clinicians and patients, we identified pivotal issues in RPM data management and patient engagement. The GenAI RPM assistant integrates a patient-facing chatbot, clinician-facing smart summaries, and automated draft portal messages to enhance communication and streamline data review. Validated through six rounds of testing and evaluations by ten participants, the initial prototype was positively received, highlighting the importance of personalized interactions. Our findings demonstrate GenAI's potential to improve RPM by optimizing data management and enhancing patient-provider communication. © 2024 The Authors.

关键词： Information management

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

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arXiv 2024年

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Hildesheim Germany Data Analytics & AI Engineering Volkswagen Financial Services AG Braunschweig Germany

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer duration. Our experiments further indicate that ProbSAINT is especially accurate on instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024, CC BY.

关键词： Costs

Robust and Efficient Writer-Independent IMU-Based Handwriting Recognization

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arXiv 2025年

作者： Li, Jindong Hamann, Tim Barth, Jens Kaempf, Peter Zanca, Dario Eskofier, Bjoern Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany STABILO International GmbH Germany

Online handwriting recognition (HWR) using data from inertial measurement units (IMUs) remains challenging due to variations in writing styles and the limited availability of high-quality annotated datasets. Traditional models often struggle to recognize handwriting from unseen writers, making writer-independent (WI) recognition a crucial but difficult problem. This paper presents an HWR model with an encoder-decoder structure for IMU data, featuring a CNN-based encoder for feature extraction and a BiLSTM decoder for sequence modeling, which supports inputs of varying lengths. Our approach demonstrates strong robustness and data efficiency, outperforming existing methods on WI datasets, including the WI split of the OnHW dataset and our own dataset. Extensive evaluations show that our model maintains high accuracy across different age groups and writing conditions while effectively learning from limited data. Through comprehensive ablation studies, we analyze key design choices, achieving a balance between accuracy and efficiency. These findings contribute to the development of more adaptable and scalable HWR systems for real-world applications. The code is available at https://***/jindongli24/REWI. Copyright © 2025, The Authors. All rights reserved.

关键词： Units of measurement

CNN-transformer mixed model for object detection

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arXiv 2022年

作者： Li, Wenshuo Machine Learning for Visual Data Analytics United Kingdom

Object detection, one of the three main tasks of computer vision, has been used in various applications. The main process is to use deep neural networks to extract the features of an image and then use the features to identify the class and location of an object. Therefore, the main direction to improve the accuracy of object detection tasks is to improve the neural network to extract features better. In this paper, I propose a convolutional module with a transformer[1], which aims to improve the recognition accuracy of the model by fusing the detailed features extracted by CNN[2] with the global features extracted by a transformer and significantly reduce the computational effort of the transformer module by deflating the feature mAP. The main execution steps are convolutional downsampling to reduce the feature map size, then self-attention calculation and upsampling, and finally concatenation with the initial input. In the experimental part, after splicing the block to the end of YOLOv5n[3] and training 300 epochs on the coco dataset, the mAP improved by 1.7% compared with the previous YOLOv5n, and the mAP curve did not show any saturation phenomenon, so there is still potential for improvement. After 100 rounds of training on the Pascal VOC dataset, the accuracy of the results reached 81%, which is 4.6 better than the faster RCNN[4] using resnet101[5] as the backbone, but the number of parameters is less than one-twentieth of it. © 2022, CC BY.

关键词： Object detection

Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine Learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing machine learning Image segmentation