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Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning
2018
International Journal of Advanced Computer Science and Applications
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Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. ...
Recommendation is very crucial technique for social networking sites and business organizations. ...
Convolutional neural network, deep feedforward model, recurrent neural network model and deep belief model are described with their relevance. ...
doi:10.14569/ijacsa.2018.091049
fatcat:duhdn5ipknbflekirpkpnpvdhi
A Review of Graph Signal Processing with Neural Networks
2022
North atlantic university union: International Journal of Circuits, Systems and Signal Processing
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For the popular topics on processing the graph data with neural networks, the main models/frameworks, dataset and applications are discussed in details. ...
In this paper, we review the development of the traditional graph signal processing methodology, and the recent research areas that are applying graph neural networks on graph data. ...
At the same time, with the great success of deep neural networks applied in image analytics and natural language processing, and the more intuitive thought is applying the deep neural networks on the large ...
doi:10.46300/9106.2022.16.91
fatcat:l6llhel3xfbhjhfkxzyoxp4oqm
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
2021
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated ...
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. ...
Listings 6: Evaluating the recurrent graph convolutional neural network with GPU based acceleration. ...
doi:10.1145/3459637.3482014
fatcat:gzljodd7c5cqjg2cdnbfawq7hq
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.07788v2 fatcat:dd53r2myfvek5c4nyofjzdygpiOther Versions
PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated ...
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. ...
Operating on a temporal graph sequence these models perform message-passing at each time point with a graph neural network block and the new temporal information is incorporated by a temporal deep learning ...
arXiv:2104.07788v3
fatcat:ktnji6kjrzd7no6blp6zimfutu
Survey on Software Tools that Implement Deep Learning Algorithms on Intel/x86 and IBM/Power8/Power9 Platforms
2019
Supercomputing Frontiers and Innovations
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But to get these results neural networks become progressively more complex, thus needing a lot more training. The training of neural networks today can take weeks. ...
Neural networks are becoming more and more popular in scientific field and in the industry. ...
Acknowledgements The results described in this paper were obtained with the financial support of the grant from the Russian Federation President Fund (MK-2330.2019.9). ...
doi:10.14529/jsfi190404
fatcat:7ou5vt4bdvdljcvzjz2jsx7osa
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
[article]
2021
arXiv
pre-print
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In this study, we focus on Graph Neural Networks (GNN) that have found great success in tasks such as node or edge classification and link prediction. ...
Many interesting datasets ubiquitous in machine learning and deep learning can be described via graphs. ...
The neural network model was implemented in PyTorch with the graph frameworks, PyTorch Geometric (PyG) (Fey & Lenssen, 2019) , and Deep Graph Library (DGL) (Wang et al., 2019) . ...
arXiv:2012.10840v2
fatcat:ygantb35i5ghrkulczmx7dyb2e
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
[article]
2018
arXiv
pre-print
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The tensor abstraction and data-flow graph, coupled with an RDMA network, offers the opportunity to reduce the unnecessary overhead (e.g., memory copy) without sacrificing programmability and generality ...
We show that RPC is sub-optimal for distributed deep learning computation, especially on an RDMA-capable network. ...
convolutional neural network (CNN), recurrent neural network (RNN) and fully connected neural network (FCN). ...
arXiv:1805.08430v1
fatcat:6ifs7r7suvdlzojyvcsocbu5ia
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
2019
USENIX Annual Technical Conference
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We present NeuGraph, a new framework that bridges the graph and dataflow models to support efficient and scalable parallel neural network computation on graphs. ...
This evolution has led to large graph-based neural network models that go beyond what existing deep learning frameworks or graph computing systems are designed for. ...
These methods, known as graph neural networks (GNNs), combine standard neural networks with iterative graph propagation: the property of a vertex is computed recursively (with neural networks) from the ...
dblp:conf/usenix/MaYMXWZD19
fatcat:zr2sgdhlefa3rj77j3hi3bsvnq
ChemicalX: A Deep Learning Library for Drug Pair Scoring
[article]
2022
arXiv
pre-print
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Our system provides neural network layers, custom pair scoring architectures, data loaders, and batch iterators for end users. ...
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. ...
It can use various architectures such as feedforward neural networks or graph neural networks to achieve this. Definition 5. Context encoder. ...
arXiv:2202.05240v3
fatcat:fikimsykfrey3kchdrezkbal5y
Solving differential equations with unknown constitutive relations as recurrent neural networks
[article]
2017
arXiv
pre-print
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Use of techniques from recent deep learning literature enables training of functions with behavior manifesting over thousands of time steps. ...
We extend TensorFlow's recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. ...
TensorFlow
Basics TensorFlow [1] is an open source software library for scalable, vectorized numerical computation. ...
arXiv:1710.02242v1
fatcat:2kuqcgfanvax5jt6bz4cmqjjpi
Scalable variational Monte Carlo with graph neural ansatz
[article]
2020
arXiv
pre-print
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Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this direction. ...
The first is graph neural ansatz (GNA), which is a variational wavefunction universal to arbitrary geometry. ...
Broader Impact This research develops a scalable variational Monte Carlo (VMC) algorithm and a universal graph neural ansatz (GNA). ...
arXiv:2011.12453v1
fatcat:vrccelrumzfvjadebwvwvjfmxu
Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks
[article]
2021
arXiv
pre-print
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interference graph neural network (HIGNN) to handle these challenges. ...
It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. ...
Sun, et al., where deep neural networks (DNNs) are
scalable to wireless networks of growing sizes with robust perfor- adopted to imitate the input-output ...
arXiv:2104.05463v2
fatcat:t4gyp47cyvhzzeemgakz7gecwu
Resources
[chapter]
2020
Representation Learning for Natural Language Processing
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However, training a deep neural network is usually a very time-intensive process and requires lots of code to build related models. ...
To alleviate these issues, some deep learning frameworks have been developed and released, which incorporate some existing and necessary arithmetic operators for neural network constructions. ...
Moreover, GraphVite is designed to be scalable. Even with limited memory, GraphVite can process node embedding task on billion-scale graphs. ...
doi:10.1007/978-981-15-5573-2_10
fatcat:qs6uihkvjnfmndbdtr32buqt7y
Review on Different Software Tools for Deep Learning
2022
International Journal for Research in Applied Science and Engineering Technology
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In this review paper we have discussed the features of some popular open source software tools available for deep learning along with their advantages and disadvantages. ...
Abstract: Deep Learning Applications are being applied in various domains in recent years. Training a deep learning model is a very time consuming task. ...
a programming support of deep neural networks and machine learning techniques. c) It includes a high scalable feature of computation with various data sets. d) TensorFlow uses GPU computing, automating ...
doi:10.22214/ijraset.2022.39873
fatcat:dyrasieoujgw3damqexfq2urfm
Representation Learning on Spatial Networks
2021
Neural Information Processing Systems
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Hence it can not be modeled merely with either spatial or network models individually. ...
Spatial networks are networks for which the nodes and edges are constrained by geometry and embedded in real space, which has crucial effects on their topological properties. ...
. • Geometric Deep Learning on Graphs. The earliest attempts we are aware of to generalize neural networks to graphs are attributed to M. Gori et at. [40] . ...
dblp:conf/nips/ZhanZ21
fatcat:djdflwk635anpf6sqyjbacbs5q
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