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共 274 条记录，以下是141-150 订阅

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Graph Augmentation for neural networks Using Matching-Graphs 10th

Graph Augmentation for Neural Networks Using Matching-Graphs

引用

10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Fuchs, Mathias Riesen, Kaspar Univ Bern Inst Comp Sci CH-3012 Bern Switzerland Univ Appl Sci Northwestern Inst Informat Syst CH-4600 Olten Switzerland

ISBN: (纸本)9783031206498;9783031206504

Both data access and data collection have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In some cases, however, the amount of data is still not sufficiently large (e.g. in some machine learning applications). Data augmentation is a widely used mechanism to increase the available data in such cases. Current augmentation methods are mostly developed for statistical data and only a small part of these methods is directly applicable to graphs. In a recent research project, a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding, termed matching-graph, is to formalize the stable cores of pairs of patterns by means of graphs. In the present paper, we propose to use these matching-graphs to augment training sets of graphs in order to stabilize the training process of state-of-the-art graph neural networks. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique is able to significantly improve the classification accuracy of three different neural network models.

关键词： Graph matching Matching-graphs Graph edit distance Graph augmentation GNN

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Deep Learning in the Wild 8th

Deep Learning in the Wild

引用

8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Stadelmann, Thilo Amirian, Mohammadreza Arabaci, Ismail Arnold, Marek Duivesteijn, Gilbert Francois Elezi, Ismail Geiger, Melanie Lorwald, Stefan Meier, Benjamin Bruno Rombach, Katharina Tuggener, Lukas ZHAW Datalab Winterthur Switzerland Sch Engn Winterthur Switzerland Ulm Univ Inst Neural Informat Proc Ulm Germany ARGUS DATA INSIGHTS Schweiz AG Zurich Switzerland Deep Impact AG Winterthur Switzerland Ca Foscari Univ Venice DAIS Venice Italy Univ Neuchatel Inst Informat Neuchatel Switzerland PricewaterhouseCoopers AG Zurich Switzerland IDSIA Dalle Molle Inst Artificial Intelligence Manno Switzerland

ISBN: (纸本)9783319999784;9783319999777

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty- gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.

关键词： Data availability Deployment Loss & reward shaping Real world tasks

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Pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data 11th

Pitfalls in Processing Infinite-Length Sequences with Popula...

引用

11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Casoni, Michele Guidi, Tommaso Tiezzi, Matteo Betti, Alessandro Gori, Marco Melacci, Stefano Univ Siena DIISM I-52100 Siena Italy IMT Scuola Alti Studi I-55100 Lucca Italy

ISBN: (纸本)9783031716010;9783031716027

One of the enduring challenges for the Machine Learning community is developing models that can process and learn from very long data sequences. Transformer-based models and Recurrent neural networks (RNNs) have excelled in processing long sequences, yet face challenges in transitioning to processing infinite-length sequences online, a crucial step in mimicking human learning over continuous data streams. While Transformer models handle large context windows efficiently, they suffer from quadratic computational costs, motivating research into alternative attention mechanisms. Conversely, RNNs, particularly Deep State-Space Models (SSMs), have shown promise in long sequence tasks, outperforming Transformers in certain benchmarks. However, current approaches are limited to finite-length sequences, which are pre-buffered and randomly shuffled to cope with stochastic gradient descent. This paper addresses the fundamental gap in transitioning from offline-processing of a dataset of sequences to online-processing of possibly infinite-length sequences, a scenario often neglected in existing research. Empirical evidence is presented, demonstrating the performance and limits of existing models. We highlight the challenges and opportunities in learning from a continuous data stream, paving the way for future research in this area.

关键词： Recurrent neural networks Transformers Long Sequences Online Learning Learning over Time

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Kernel estimators and mixture models in artificial neural networks

Kernel estimators and mixture models in artificial neural ne...

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Proceedings of the 3rd workshop on neural networks: Academic/Industrial/NASA/Defense

作者： Priebe, Carey E. Marchette, David J. Rogers, George W. Solka, Jeffrey L. Naval Surface Warfare Cent Dahlgren United States

ISBN: (纸本)1565550072

For many pattern recognition problems, including unsupervised learning applications, a direct probability density estimate is more powerful than a posterior probability estimate. Kernel estimation and finite mixture models represent two well-known methods of performing probability density estimation. It is shown that kernel and mixture estimates can be performed in the neural network style (e.g. massively parallel, local computations.) The convergence properties of these estimates are well known, and thus a network performing such an estimate has inherently desirable asymptotic characteristics both in terms of functional estimation and optimal classification performance. Several common artificial neural network (ANN) paradigms, including adaptive resonance theory, radial basis functions, probabilistic neural nets, and adaptive kernel neural networks, can be described in terms of their pattern recognition capabilities, as kernel and mixture estimates.

关键词： neural networks

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Medical Deepfake Detection using 3-Dimensional neural Learning 10th

Medical Deepfake Detection using 3-Dimensional Neural Learni...

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10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Sharafudeen, Misaj Chandra, S. S. Vinod Univ Kerala Dept Comp Sci Thiruvananthapuram Kerala India

ISBN: (纸本)9783031206498;9783031206504

In recent years, Generative Adversarial networks (GAN) have underlined the necessity for exercising caution in trusting digital information. Injection and removal of tumorous nodules from medical imaging modalities is one method of maneuvering deepfakes. The inability to acknowledge medical deepfakes can result in a substantial impact on healthcare procedures or even cause of death. With a systematic case study, this work seeks to address the detection of such assaults in lung CT (Computed Tomography) scans generated using CT-GANs. We experiment with machine learning methods and a novel 3-dimensional deep neural architecture on the topic of differentiating between tampered and untampered data. The proposed architecture on the CT-GAN dataset attained a remarkable accuracy of 91.57%, sensitivity of 91.42%, and specificity of 97.20%. Sectioned data cubes containing the affected region of interest seem to perform better compared to raw CT slices with a gain of approximately 20%. Furthermore, 3DCNN outperforms its 2dimensional counterpart as it extracts temporal features unlike the spatial relationship insufficient for medical data processing. The outcomes of this research reveal that nodule injection and removal manipulations in complicated CT slices may be recognized with a high degree of precision.

关键词： Deepfake detection Computed tomography Fake cancer nodules Machine learning Deep learning

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artificial neural networks in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Luca Pancioni Edmondo Trentin Friedhelm Schwenker

ISBN: (数字)9783319999784

ISBN: (纸本)9783319999777

This book constitutes the refereed proceedings of the 8th iapr TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2018, held in Siena, Italy, in September 2018.;The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.;Chapter "Bounded Rational Decision-Making with Adaptive neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via ***.

关键词： artificial Intelligence pattern recognition Image Processing and Computer Vision Natural Language Processing (NLP) Data Mining and Knowledge Discovery

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Disparity using feature points in multi scale 9th

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Joint iapr 9th International workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th International workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Ulusoy, Ilkay Hancock, Edwin R. Halici, Ugur Computer Vision and Artificial Neural Networks Lab Middle East Technical University Ankara Turkey Department of Computer Science Uni versity of York YorkY01 5DD United Kingdom

ISBN: (纸本)3540440119

In this paper we describe a statistical framework for binocular disparity estimation. We use a bank of Gabor filters to compute multiscale phase signatures at detected feature points. Using a von Mises distribution, we calculate correspondence probabilities for the feature points in different images using the phase differences at different scales. The disparity map is computed using the set of maximum likelihood correspondences. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Gabor filters

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Manifold Learning Regression with Non-stationary Kernels 8th

Manifold Learning Regression with Non-stationary Kernels

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8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Kuleshov, Alexander Bernstein, Alexander Burnaev, Evgeny Skolkovo Inst Sci & Technol Skolkovo Innovat Ctr 3 Nobel St Moscow 121205 Russia

ISBN: (纸本)9783319999784;9783319999777

Nonlinear multi-output regression problem is to construct a predictive function which estimates an unknown smooth mapping from q-dimensional inputs to m-dimensional outputs based on a training data set consisting of given "input-output" pairs. In order to solve this problem, regression models based on stationary kernels are often used. However, such approaches are not efficient for functions with strongly varying gradients. There exist some attempts to introduce non-stationary kernels to account for possible non-regularities, although even the most efficient one called Manifold Learning Regression (MLR), which estimates the unknown function as well its Jacobian matrix, is too computationally expensive. The main problem is that the MLR is based on a computationally intensive manifold learning technique. In this paper we propose a modified version of the MLR with significantly less computational complexity while preserving its accuracy.

关键词： Nonlinear multi-output regression Manifold learning regression Non-stationary kernel

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An improved handwritten Chinese character recognition system using support vector machine

An improved handwritten Chinese character recognition system...

引用

1st iapr TC3 workshop on artificial neural networks in pattern recognition

作者： Dong, JX Krzyzak, A Suen, CY Ctr Pattern Regcognit & Machine Intelligence Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

This paper describes several techniques improving a Chinese character recognition system. Enhanced nonlinear normalization, feature extraction and tuning kernel parameters of support vector machine on a large data set with thousands of classes, contribute to improvement of the overall system performance. The enhanced nonlinear normalization method not only solves the aliasing problem in the original Yamada et al.'s nonlinear normalization method but also avoids the undue stroke distortion in the peripheral region of the normalized image. The support vector machine is for the first time tested on a large data set composed of several million samples and thousands of classes. The recognition system has achieved a high recognition rate of 99.0% on ETL9B, a handwritten Chinese character database. (c) 2005 Elsevier B.V. All rights reserved.

关键词： normalization support vector machine Chinese characters recognition feature extraction

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Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional neural networks 7th

Objectness Scoring and Detection Proposals in Forward-Lookin...

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7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Valdenegro-Toro, Matias Heriot Watt Univ Sch Engn & Phys Sci Ocean Syst Lab Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9783319461823;9783319461816

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94% recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

关键词： Object detection Detection proposals Sonar image processing Forward-looking sonar

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