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A Gentle Introduction to Deep Learning for Graphs
[article]
2019
arXiv
pre-print
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This work is designed as a tutorial introduction to the field of deep learning for graphs. ...
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. ...
This paper takes pace from this historical perspective to provide a gentle introduction to the field of neural networks for graphs, also referred to as deep learning for graphs in modern terminology. ...
arXiv:1912.12693v1
fatcat:lww6akhmuvbe5hu335hl7ggghu
Deep Adversarial Learning for
2019
Proceedings of the 2019 Conference of the North
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In this tutorial, we provide a gentle introduction to the foundation of deep adversarial learning, as well as some practical problem formulations and solutions in NLP. ...
We describe recent advances in deep adversarial learning for NLP, with a special focus on generation, adversarial examples & rules, and dialogue. ...
In particular, we start with the gentle introduction to the fundamentals of adversarial learning. ...
doi:10.18653/v1/n19-5001
dblp:conf/naacl/WangSL19
fatcat:d4epvsksbnhmbakslrbqr5exhu
Deep Reinforcement Learning for NLP
2018
Proceedings of ACL 2018, Tutorial Abstracts
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In this tutorial, we provide a gentle introduction to the foundation of deep reinforcement learning, as well as some practical DRL solutions in NLP. ...
We describe recent advances in designing deep reinforcement learning for NLP, with a special focus on generation, dialogue, and information extraction. ...
In particular, we start with the gentle introduction to the fundamentals of reinforcement learning (Sutton and Barto, 1998; Sutton et al., 2000) . ...
doi:10.18653/v1/p18-5007
dblp:conf/acl/WangLH18
fatcat:otigakcyzrgwlhcwbigvdp7qgm
DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network
2022
Mathematics
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Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation ...
Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. ...
The learning curves for the Top 10 were more gentle than the Top 100 in the early steps, which showed that the model mainly focused on recalling more reliable candidate items. ...
doi:10.3390/math10050721
fatcat:z3jylmbkgzfrziwf3zc5fcluci
Latent Structure Models for Natural Language Processing
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
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It will offer a gentle introduction to recent advances in structured modeling with discrete latent variables, which were not previously covered in any ACL/EMNLP/IJCNLP/NAACL related tutorial. ...
. • machine learning: familiarity with neural networks for NLP, basic understanding of backpropagation and computation graphs. ...
doi:10.18653/v1/p19-4001
dblp:conf/acl/MartinsMNN19
fatcat:5vdzt5p23ncqrcngougdsddcx4
A Gentle Introduction to Graph Neural Networks
2021
Distill
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For attribution in academic contexts, please cite this work as
Sanchez-Lengeling, et al., "A Gentle Introduction to Graph Neural Networks", Distill, 2021. ...
BibTeX citation
@article{sanchez-lengeling2021a, author = {Sanchez-Lengeling, Benjamin and Reif, Emily and Pearce, Adam and Wiltschko, Alex}, title = {A Gentle Introduction to Graph Neural Networks ...
doi:10.23915/distill.00033
fatcat:7sa54cvytzecnm3ctjxa4mj3ta
Transfer learning-based Plant Disease Detection
2021
International journal for innovative engineering and management research
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Deep Neural Networks in the field of Machine Learning (ML) are broadly used for deep learning. ...
This study provides a transfer learning-based solution for detecting multiple diseases in several plant varieties using simple leaf images of healthy and diseased plants taken from PlantVillage dataset ...
Introduction Today, modern technology allows us to grow crops in quantities necessary for a gentle food supply for billions of individuals. ...
doi:10.48047/ijiemr/v10/i03/99
fatcat:7gwa3st6szdqtbcl5kp26vqbje
Structural Deep Clustering Network
[article]
2020
arXiv
pre-print
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for clustering is a crucial requirement. ...
Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep ...
Hence learning an effective data representation is a crucial prerequisite for deep clustering. ...
arXiv:2002.01633v2
fatcat:c2l57f6rszf5pfrdwcncaabbnm
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous ...
Since the choices of selecting representative frames are multitudinous for each video, we model the frame selection as a progressive process through deep reinforcement learning, during which we progressively ...
Moreover, we employ a graph-based deep learning model to capture both the intrinsic and extrinsic dependencies between human joints. ...
doi:10.1109/cvpr.2018.00558
dblp:conf/cvpr/TangTLL018
fatcat:gv6x3vml7fe3peiabn5qxa763y
Near Maximum Likelihood Decoding with Deep Learning
[article]
2018
arXiv
pre-print
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Simulations of the hessian and the condition number show why the learning process is accelerated. We demonstrate the decoding algorithm for various linear block codes of length up to 63 bits. ...
A novel and efficient neural decoder algorithm is proposed. The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group. ...
INTRODUCTION In the last few years Deep Learning methods were applied to communication systems, for example in [1]- [8] . ...
arXiv:1801.02726v1
fatcat:gmdncv26e5gorfpta76awjfv5y
Deep Learning and Open Set Malware Classification: A Survey
[article]
2020
arXiv
pre-print
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This survey provides an overview of different deep learning techniques, a discussion of OSR and graph representation solutions and an introduction of malware classification systems. ...
The dramatic increase of malware has led to a research area of not only using cutting edge machine learning techniques classify malware into their known families, moreover, recognize the unknown ones, ...
CONCLUSIONS We provide a brief introduction of several deep neural network structures, and an overview of existing OSR, a discussion on learning graph representation and malware classification in this ...
arXiv:2004.04272v1
fatcat:332sfs7davh2hkxoehiyjta2y4
A Gentle Introduction to Deep Learning in Medical Image Processing
[article]
2018
arXiv
pre-print
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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. ...
Doing so allows us to understand the reasons for the rise of deep learning in many application domains. ...
Acknowledgements We express our thanks to Katharina Breininger, Tobias Würfl, and Vincent Christlein, who did a tremendous job when we created the deep learning course at the University of Erlangen-Nuremberg ...
arXiv:1810.05401v2
fatcat:dtd5eyj65jbfdjtsywxw3ilaqq
DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network
2020
Remote Sensing
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However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. ...
Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. ...
Acknowledgments: The authors thank Guangzhou Jiantong Surveying, Mapping, and Geographic Information Technology Ltd. for providing the data used in this research project. ...
doi:10.3390/rs12010178
fatcat:py7wp4767fejdphfkye7rmp4xe
A modified technique for recognizing facial expression
2020
Journal of Applied Research on Industrial Engineering
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This facial region is passed on to the model trained by a CNN where facial features are matched with the features specified in the model. ...
In the initial processing stage the facial region is identified using a Haar cascade classifier. ...
An & Liu [49] 2019 The authors use deep learning to recognize facial expressions, they proposed a new function to initialize CNN and LSTM networks. Kimet al. ...
doi:10.22105/jarie.2020.259293.1216
doaj:79760e41d6564578ad18953c40756591
fatcat:wuwtd6x5qbhd3fdbfwvqrskpqu
DEM Void Filling Based on Context Attention Generation Model
2020
ISPRS International Journal of Geo-Information
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Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. ...
A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training ...
The introduction of a discriminate model leads to the emergence of a game type training process. ...
doi:10.3390/ijgi9120734
fatcat:lovvtxo3kjgufaagk5gemy4v3i
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