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Natural Language QA Approaches using Reasoning with External Knowledge [article]

Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra
2020 arXiv   pre-print
Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's "councilmen" example in his 1972 paper and McCarthy's Mr.  ...  Hug example of 1976 highlights the role of external knowledge in NL understanding.  ...  Figure 2 : 2 Heterogeneous Graph Neural Network with Structured Knowledge. The example is taken from WikiHop dataset.  ... 
arXiv:2003.03446v1 fatcat:5ssmvcdzajc5flasg3s5hsfxsu

Chatbots Employing Deep Learning for Big Data

So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks.  ...  Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders.  ...  The Convolution Neural Network(CNN) and Recurrent Neural Network (RNN) are Deep Neural Network (DNN) architectures can be adapted from the neural machine translation domain, where it already outperforms  ... 
doi:10.35940/ijitee.i8017.0981119 fatcat:eszidvr2gndafbomcxvkhnhyiu

Estimating Large-Scale Network Convergence in the Human Functional Connectome

Peter T. Bell, James M. Shine
2015 Brain Connectivity  
Finally, we examined the convergence of systems within each of the individual resting-state networks in the brain, revealing the heterogeneity by which individual resting-state networks balance the competing  ...  Although investigating specialized resting-state networks has led to significant advances in our understanding of brain organization, the manner in which information is integrated across these networks  ...  These findings are consistent with the prediction that transmodal regions will contain amalgamated neural signals that partially correlate with signals arising from their input networks (Mesulam, 1998  ... 
doi:10.1089/brain.2015.0348 pmid:26005099 fatcat:z5or6bkqmvaelidenymtjjuuki

Distilling Knowledge from Graph Convolutional Networks [article]

Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, Xinchao Wang
2021 arXiv   pre-print
Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph convolutional networks  ...  In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a pre-trained GCN model.  ...  Knowledge Amalgamation. Knowledge amalgamation [33, 22, 32, 44] aims to learn a student network from multiple teachers from different domains.  ... 
arXiv:2003.10477v4 fatcat:vtwlv7dsjrefxhhxmwtqbufrdu

Knowledge Amalgamation for Object Detection with Transformers [article]

Haofei Zhang, Feng Mao, Mengqi Xue, Gongfan Fang, Zunlei Feng, Jie Song, Mingli Song
2022 arXiv   pre-print
Currently, most of these approaches are tailored for convolutional neural networks (CNNs).  ...  Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student.  ...  Nowadays, most KA works are proposed in the realm of CNNs, apart from [23] for graph neural networks.  ... 
arXiv:2203.03187v1 fatcat:xf7i6u27erbozb3cuge6fqni5e

Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication

Payal Mahajan, Zaheeruddin Zaheeruddin
2020 International Journal of Electrical and Computer Engineering (IJECE)  
The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%.  ...  Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible.  ...  Figure 1 . 1 Heterogeneous Analysis of back propagation and radial basis function neural networks for handover ....  ... 
doi:10.11591/ijece.v10i5.pp4835-4843 fatcat:v4xm65ii7vd3hgo2d6tpybzdlm

Recent Development in Disease Diagnosis by Information, Communication and Technology

Shabana Urooj, Astha Sharma, Chitransh Sinha, Fadwa Alrowais
2020 Journal of Clinical and Diagnostic Research  
In the field of healthcare related instrumentation, AI plays a prevalent role with the amalgamation of several technological progressions.  ...  Bruna J et al., Graph Neural Networks (GNNs) A Graph Convolutional Network (GCN) is a GNN that enables CNNs to directly operate on graphs and updates feature vectors of nodes based on the properties of  ...  Experts systems can benefit from data mining techniques to form knowledge base.  ... 
doi:10.7860/jcdr/2020/43331.13535 fatcat:ckz6wcgmjnan5fkiok5zvoywky

Knowledge Distillation: A Survey [article]

Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao
2021 arXiv   pre-print
In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks.  ...  This paper provides a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance  ...  It is usually required to transfer knowledge from deeper and wider neural networks to shallower and thinner neural networks (Romero et al., 2015) .  ... 
arXiv:2006.05525v6 fatcat:aedzaeln5zf3jgjsgsn5kvjrri

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Bilal Saeed Raja, Sohail Asghar
2020 International Journal of Advanced Computer Science and Applications  
Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their  ...  Fuzzy logic was amalgamated with the modular neural network to diagnose the risks of hypertension [30] .  ...  Artificial Neural Networks (ANNs) Artificial neural networks are mathematical models that mimic the human brain in the learning process [22] .  ... 
doi:10.14569/ijacsa.2020.0110287 fatcat:otlc5hq56jdifnzefch2kh2ca4

An Amalgam Method To Detect And Classify Brain Tumor Using MRI Images By Fuzzy C Means, Discrete Wavelet Transform and Artificial Neural Network

Ganesh Kumar B, Saurabh Shukla, Shravya N, Yathish V, Sandesh Kumar .B.V
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
It is used to further reduce the feature matrix. 13 parameters are obtained from after performing feature extraction which are used as an input for Artificial Neural Network.  ...  In our approach, an automatic brain tumor detection and classification framework that consists of techniques based on Fuzzy C means clustering, Discrete Wavelet Transforms and Artificial neural network  ...  This is done using Artificial Neural network. Feed forward network is used such that the features propagate in the forward direction through the neural network.  ... 
doi:10.23956/ijarcsse/sv7i5/0332 fatcat:uqj5syt2hbftvbdsjvyugxpgaa

Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks [article]

Lin Wang, Kuk-Jin Yoon
2021 arXiv   pre-print
To achieve faster speeds and to handle the problems caused by the lack of data, knowledge distillation (KD) has been proposed to transfer information learned from one model to another.  ...  Deep neural models in recent years have been successful in almost every field, including extremely complex problem statements.  ...  The knowledge graph includes two triplets < h, r, t >: < LosAngeles, IsCityOf, Calif ornia > and < Calif ornia, isStateOf, U S >. Graph neural networks.  ... 
arXiv:2004.05937v6 fatcat:yqzo7nylzbbn7pfhzpfc2qaxea

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
INDEX TERMS Context-aware system, Contextual factors, Recommender system, Social network Areej Bin-Suhaim received the B.S. degree in information technology from King Saud University (KSU), Riyadh, Saudi  ...  She also has a master degree in information systems from KSU. She is currently pursuing the Ph.D. degree with the Department of Information Systems from KSU.  ...  The graph-attention neural network proposed in [99] relies on dynamic user's behaviors with recurrent neural network (RNN) and context-dependent social influence to model user's session-based interest  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

Telemetry Based Anomaly Detection and Correlation in Data Center

2020 International journal of recent technology and engineering  
Data center is a complex amalgamation of servers where there are thousands of services, storage, networking, routers, switches and softwares providing services 24x7 to customers.  ...  With the use of Artificial Neural networks a trained model can provide solutions with high accuracy and scalablility which result in higher uptime and reduced MTTR for customers.  ...  A neural network is modeled after biological neural networks. Human brain has about 100 billion neurons.  ... 
doi:10.35940/ijrte.a2725.059120 fatcat:kmjlmanrzzckdpczkq23ldui4q

Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking [article]

Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman
2020 arXiv   pre-print
Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances.  ...  Relevant developments in machine learning research on graphs is surveyed, for this purpose.  ...  [36] 2020 categorization of graph neural networks into: recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, spatial-temporal graph neural networks, discussion of  ... 
arXiv:2005.11081v1 fatcat:ajqghcevqvdrvdlcrknxlzlqdi

The Diagrammatic AI Language (DIAL): Version 0.1 [article]

Guy Marshall, André Freitas
2018 arXiv   pre-print
Of the papers in some way concerned with Neural Networks, 78% include a diagram of a Neural Network. Table 1 shows the distribution of Neural Network diagrams with each conceptual property.  ...  Taking the sub-area of Neural Networks as an example, a large variety of neural architectures have emerged recently.  ... 
arXiv:1812.11142v1 fatcat:uzaqojpncbbpvivnipvxtiqlfe
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