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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine learning, ESANN 2020

作者： Qaraei, Mohammadreza Khandagale, Sujay Babbar, Rohit Aalto University CS Department Helsinki Finland Columbia University CS Department New York United States

ISBN: (纸本)9782875870742

Extreme Multi-label Text Classification (XMTC) refers to supervised learning of a classifier which can predict a small subset of relevant labels for a document from an extremely large set. Even though deep learning algorithms have surpassed linear and kernel methods for most natural language processing tasks over the last decade;recent works show that state-of-the-art deep learning methods can only barely manage to work as well as a linear classifier for the XMTC task. The goal of this work is twofold: (i) to investigate the reasons for the comparable performance of these two strands of methods for XMTC, and (ii) to document this observation explicitly, as the efficacy of linear classifiers in this regime, has been ignored in many relevant recent works. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine learning.

关键词： learning algorithms

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A deep learning algorithm for detection of potassium deficiency in a red grapevine and spraying actuation using a raspberry pi3

A deep learning algorithm for detection of potassium deficie...

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2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020

作者： Ukacgbu, Uchechi Tartibu, Lagouge Laseinde, Timothy Okwu, Modestus Olayode, Isaac Department of Mechanical and Industrial Engineering University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781728167701

The fourth industrial revolution (4IR) has ushered in advancement, which is currently reshaping all sectors of the economy. including the agricultural domain. This paper describes the application of artificial intelligence technique on an embedded device. It involves the smart detection of potassium deficiency in red grape vines using the deep learning algorithm. This was deployed on a raspberry pi-3 for real-time actuation and effective prediction. The light-emitting diode (LED) was lit when a potassium deficient red grapevine leaf was brought close to the pi-camera. Image data obtained was fed as input into the model. Training. validation, and testing accuracies of 89%, 81 and 80% were obtained respectively for the CNN model which surpassed the performance of the Support Vector Machines (SVM) classifier. This research has demonstrated a paradigm shift from the conventional agricultural method of detecting nutrient deficiency to a more effective real-time deep learning algorithm which prompt a corresponding actuation to effectively spray of fertilizers. This technique in no doubt would lead to tremendous increase in food production. © 2020 IEEE.

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Chest X-Ray Report Generation Through Fine-Grained Label learning 23rd

Chest X-Ray Report Generation Through Fine-Grained Label Lea...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Syeda-Mahmood, Tanveer Wong, Ken C. L. Gur, Yaniv Wu, Joy T. Jadhav, Ashutosh Kashyap, Satyananda Karargyris, Alexandros Pillai, Anup Sharma, Arjun Syed, Ali Bin Boyko, Orest Moradi, Mehdi IBM Almaden Research Center San JoseCA United States

ISBN: (纸本)9783030597122

Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established metrics. © 2020, Springer Nature Switzerland AG.

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Predictive iterative learning control with data-driven model for optimal laser power in selective laser sintering 29

Predictive iterative learning control with data-driven model...

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29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2018

作者： Nettekoven, A. Fish, S. Topcu, U. Beaman, J. Department of Mechanical Engineering University of Texas at Austin AustinTX United States Department of Aerospace Engineering University of Texas at Austin AustinTX United States

Building high quality parts is still a key challenge for Selective Laser Sintering machines today due to a lack of sufficient process control. In order to improve process control, we propose a Predictive Iterative learning Control (PILC) controller that minimizes the deviation of the post-sintering temperature profile of a newly scanned part from a desired temperature. The controller does this by finding an optimal laser power profile and applying it to the plant in a feedforward manner. The PILC controller leverages machine learning models that accurately capture the process' temperature dynamics based on in-situ measurement data while still guaranteeing low computational cost. We demonstrate the controller's performance in regards to the control objective with heat transfer simulations by comparing the PILC-controlled laser power profiles to constant laser power profiles. © Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2018. All Rights Reserved.

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Training and validation of a commercial deep learning contouring platform 20

Training and validation of a commercial deep learning contou...

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20th Biannual Micro-Mini and Nano Dosimetry and Innovative Technologies in Radiation Oncology, MMND ITRO 2020

作者： Koo, J. Caudell, J. Feygelman, V. Moros, E. Latifi, K. Moffitt Cancer Center TampaFL33612 United States

We present our experience with training and validation of a commercially available deep learning algorithm for organs at risk(OAR) auto-contouring. Computed tomography(CTs) with OARs from a cohort of 213 head and neck(HN) patients were used for training the deep learning model. A separate cohort of 85 CTs and structure sets was used for validation. All OARs (13) were contoured by a single physician. Metrics such as the DICE similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and volumetric difference (VD) were used to analyze contouring variation. Mean DSC and JSC values ranged 0.48-0.89 and 0.32-0.8, respectively, depending on OAR. A DSC value 0.7 indicated low inter-observer variability. In our study, all but one of the contours were above this threshold. DSC for the middle pharyngeal constrictor had the lowest value of all the contours. This may be due to the small volume of this structure. Qualitative assessment of auto-segmented structure samples confirmed the reliability of DSC by demonstrating the compatibility between the expert's evaluation and DSC values. Overall, we found that deep learning auto contouring is a useful tool to speed up the process of contouring in radiotherapy treatment planning. © Published under licence by IOP Publishing Ltd.

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Research on Deep learning Algorithm and Application Based on Convolutional Neural Network 1

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Annual International Conference on 3D Imaging Technology, IC3DIT 2019

作者： Guo, Mei Xiao, Min Yu, Fang College of Software and Communication Engineering Xiangnan University ChenzhouHunan423000 China

ISBN: (数字)9789811538636

ISBN: (纸本)9789811538629

The field of artificial intelligence has developed rapidly this year, and new high-tech companies with their main business have sprung up. After years of theoretical knowledge accumulation and computer hardware equipment upgrade, deep learning began to show its talents in the field of artificial intelligence such as computer vision and voice processing. Deep learning is mainly based on multi-layer neural network to imitate, learn, and apply images, texts, and voices. As one of the deep learning algorithms, convolutional neural networks are favored by researchers in related fields because of their local connections and the advantages of weight sharing. On the basis of expounding the deep learning algorithm of convolutional neural networks at home and abroad, this paper analyzes and summarizes the application of deep learning algorithm by convolutional neural networks and prospects its development trend, hoping to engage related convolutional neural networks. Provided a reference for researchers. © Springer Nature Singapore Pte Ltd. 2020.

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Transfer learning via l1 regularization 34

Transfer learning via l1 regularization

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34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Takada, Masaaki Fujisawa, Hironori Toshiba Corporation Tokyo105-0023 Japan The Institute of Statistical Mathematics Tokyo190-8562 Japan

Machine learning algorithms typically require abundant data under a stationary environment. However, environments are nonstationary in many real-world applications. Critical issues lie in how to effectively adapt models under an ever-changing environment. We propose a method for transferring knowledge from a source domain to a target domain via l1 regularization in high dimension. We incorporate l1 regularization of differences between source parameters and target parameters, in addition to an ordinary l1 regularization. Hence, our method yields sparsity for both the estimates themselves and changes of the estimates. The proposed method has a tight estimation error bound under a stationary environment, and the estimate remains unchanged from the source estimate under small residuals. Moreover, the estimate is consistent with the underlying function, even when the source estimate is mistaken due to nonstationarity. Empirical results demonstrate that the proposed method effectively balances stability and plasticity. © 2020 Neural information processing systems foundation. All rights reserved.

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Procedure Completion by learning from Partial Summaries 31

Procedure Completion by Learning from Partial Summaries

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31st British Machine Vision Conference, BMVC 2020

作者： Naing, Zwe Elhamifar, Ehsan MCADS Lab Khoury College of Computer Sciences Northeastern University BostonMA United States

We address the problem of procedure completion in videos, which is to find and localize all key-steps of a task given only a small observed subset of key-steps. We cast the problem as learning summarization from partial summaries that allows to incorporate prior knowledge and learn from incomplete key-steps. Given multiple pairs of (video, subset of key-steps), we address the problem by learning representations of input data and finding the remaining key-steps that generalizes well to key-step discovery in new videos. We propose a loss function on the parameters of a network that promotes to recover unseen key-steps that together with the observed key-steps optimize a desired subset selection criterion. To tackle the highly non-convex learning problem, involving both discrete and continuous variables, we develop an efficient learning algorithm that alternates between representation learning and recovering unseen key-steps while incorporating prior knowledge, via a greedy algorithm. By extensive experiments on two instructional video datasets, we show the effectiveness of our framework. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

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Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization 1

Seeing stars when there aren't many stars: Graph-based semi-...

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1st Workshop on Graph-Based algorithms for Natural Language Processing, Textgraphs 2006 at Human Language Technologies

作者： Goldberg, Andrew B. Zhu, Xiaojin Computer Sciences Department University of Wisconsin-Madison Madison WI53706 United States

We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., "4 stars"), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve rating-inference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training. © 2006 Association for Computational Linguistics

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Introducing principles of synaptic integration in the optimization of deep neural networks

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NATURE COMMUNICATIONS 2022年第1期13卷 1885-1885页

作者： Dellaferrera, Giorgia Wozniak, Stanislaw Indiveri, Giacomo Pantazi, Angeliki Eleftheriou, Evangelos IBM Res Zurich Ruschlikon Switzerland Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Axelera AI High Tech Campus 5 NL-5656 AE Eindhoven Netherlands

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mechanism leads to a substantial improvement of the performance of artificial and spiking networks with feedforward, convolutional, and recurrent architectures, it mitigates catastrophic forgetting, and it is optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks. Tasks involving continual learning and adaptation to real-time scenarios remain challenging for artificial neural networks in contrast to real brain. The authors propose here a brain-inspired optimizer based on mechanisms of synaptic integration and strength regulation for improved performance of both artificial and spiking neural networks.

关键词： learning algorithms Computational science

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