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Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
-DAY 3 -Jan 14, 2021 Wang, Chen; Deng, Chengyuan 2336 OS T1.5 On the Global Self-Attention Mechanism for Graph Convolutional Networks DAY 3 -Jan 14, 2021 Track 3: Computer Vision and Analysis  ...  Semantically Extended Graph Convolutional Network DAY 2 -Jan 13, 2021 Xin, Yuan; Xu, Linli; Guo, Junliang; Li, Jiquan; Sheng, Xin; Zhou, Yuanyuan 2456 Label Incorporated Graph Neural Networks  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

A theory of memory retrieval

Roger Ratcliff
1978 Psychological review  
The theory is applied to four item recognition paradigms (Sternberg, prememorized list, study-test, and continuous) and to speed-accuracy paradigms; results are found to provide a basis for comparison  ...  It is noted that neural network models can be interfaced to the retrieval theory with little difficulty and that semantic memory models may benefit from such a retrieval scheme.  ...  Error bars are one standard deviation, ms = msec.Group reaction time distributions (bar graphs) and theoretical fits (dots) for correct rejections by set size and for hits by set size and serial position  ... 
doi:10.1037/0033-295x.85.2.59 fatcat:pi7dmmlyr5ggvjvvexnvimnc7q

A review of modularization techniques in artificial neural networks

Mohammed Amer, Tomás Maul
2019 Artificial Intelligence Review  
Modular neural networks (MNNs) are neural networks that embody the concepts and principles of modularity. MNNs adopt a large number of different techniques for achieving modularization.  ...  to emphasise the strengths and weaknesses of different modularization approaches in order to highlight good practices for neural network practitioners.  ...  networks as undirected graphs.  ... 
doi:10.1007/s10462-019-09706-7 fatcat:g4xp6dktvncu5dao53dcvoexoa

Applications of the Free Energy Principle to Machine Learning and Neuroscience [article]

Beren Millidge
2021 arXiv   pre-print
We go on to propose novel and simpler algorithms which allow for backprop to be implemented in purely local, biologically plausible computations.  ...  A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy.  ...  Discrete state-space active inference therefore optimizes two complementary objective functions.  ... 
arXiv:2107.00140v1 fatcat:c6phd65xwfc2rcyq7pnth5a3pq

A Survey on Trust Metrics for Autonomous Robotic Systems [article]

Vincenzo DiLuoffo, William R.Michalson
2021 arXiv   pre-print
This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems.  ...  Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified  ...  In order to handle complex data for AI models to be useful the community is leveraging Convolution Neural Networks (CNNs).  ... 
arXiv:2106.15015v2 fatcat:wltnmhxl5vghnopdxs36v2gpcm

Engineering, Technology & Applied Science Research (ETASR), Vol. 11, No. 2, pp. 6845-7068 [article]

Various
2021 Zenodo  
ISSN: 1792-8036 and 2241-4487.  ...  of science application, technology, and engineering.  ...  ACKNOWLEDGMENT The authors are grateful to the Institute for Research and Development, Suan Sunandha Rajabhat University and the Faculty of Science at Ubon Ratchathani University, for supporting this research  ... 
doi:10.5281/zenodo.4720665 fatcat:mk2prflstjaa3bhkjenwy22s6u

A survey on Machine Learning Techniques for Routing Optimization in SDN

Rashid Amin, Elisa Rojas, Aqsa Aqdus, Sadia Ramzan, David Casillas-Perez, Jose M. Arco
2021 IEEE Access  
More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it.  ...  The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency  ...  [126] , [127] , is a new type of Graph Neural Network (GNN) specifically conceived for modeling computer networks.  ... 
doi:10.1109/access.2021.3099092 fatcat:flp25cn2mbhohjxvuxgfupflny

MULTIMODAL ANALYSIS: Informed content estimation and audio source separation [article]

Gabriel Meseguer-Brocal
2021 arXiv   pre-print
Our study focuses on the audio and lyrics interaction for targeting source separation and informed content estimation.  ...  Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics.  ...  than the original S I to filter the data of student For inference, the network would progress frame-by-frame.  ... 
arXiv:2104.13276v3 fatcat:wirjfj4iwjgfteejmeujydey7u

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Ted J. Biggerstaff
2012 Annals of Software Engineering  
While not a silver bullet, technology is not without its contribution and the degree of payoff for any specific technology is sensitive to many factors.  ...  The record of success is mixed and the evidence is sketchy.  ...  For example within the domain of mathematical graphs, CycleChecking is a collaboration among the classes Graph, Vertex, and Workspace.  ... 
doi:10.1023/a:1018924407841 fatcat:yop2jmwtprhifjxhxnet7xbbue

Scaling Laws for Deep Learning [article]

Jonathan S. Rosenfeld
2021 arXiv   pre-print
We first demonstrate that DL training and pruning are predictable and governed by scaling laws -- for state of the art models and tasks, spanning image classification and language modeling, as well as  ...  for state of the art model compression via iterative pruning.  ...  of models for deployment and inference.  ... 
arXiv:2108.07686v1 fatcat:yg3pc6uu6bgofjvimfcfl4vhie

Rapid Prototyping for Audio Applications

David Garcia, Xavier Serra
2007 Zenodo  
Visual prototyping means that a developer can build a working application, including user interface and processing core, just by assembling elements together and changing their properties in a visual way  ...  the processing core, and, it still fulfils the real-time requirements of audio applications.  ...  Therefore, we can describe a 4MPS system as a set of Processing objects connected in graphs called Networks (see Figure 3 .22).  ... 
doi:10.5281/zenodo.3743158 fatcat:aatnhyozdzb3liwxxsmogmagk4

Deconstructing Legal Text_Object Oriented Design in Legal Adjudication [article]

Megan Ma, Dmitriy Podkopaev, Avalon Campbell-Cousins, Adam Nicholas
2020 arXiv   pre-print
In large part, law may be a ripe field for expert systems and machine learning. For engineers, existing law appears formulaic and logically reducible to "if, then" statements.  ...  The underlying assumption is that the legal language is both self-referential and universal.  ...  to regard AI as complementary,49 rather than substitutive, of legal actors.  ... 
arXiv:2009.06054v1 fatcat:h45cmonyb5favavzt477iht42u

An Introduction to Sensor Data Analytics [chapter]

Charu C. Aggarwal
2012 Managing and Mining Sensor Data  
A vast body of research work has utilized probabilistic models for computing inferred values.  ...  Network protocols ensure the effectiveness of communication between sensor nodes and provide the foundation for WSN applications.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1007/978-1-4614-6309-2_1 fatcat:pfbx566yfzgqpnjcuzonmxr23q

Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping [article]

James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz
2021 arXiv   pre-print
And when using K-FAC as the optimizer, we achieve similar results for networks without skip connections.  ...  In our experiments we show that DKS enables SGD training of residual networks without normalization layers on Imagenet and CIFAR-10 classification tasks at speeds comparable to standard ResNetV2 and Wide-ResNet  ...  Acknowledgments We would like to thank Alex Botev, Alex Graves, Georg Ostrovski, Guodong Zhang, Ilja Kuzborskij, Koray Kavukcuoglu, Neil Rabinowitz, Soham De, Yann Dauphin, and Yee Whye Teh for their guidance  ... 
arXiv:2110.01765v1 fatcat:p4dxhkmczngubjt6xhzdgrlf7q

End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings [article]

Eliot Brenner, Jun Zhao, Aliasgar Kutiyanawala, Zheng Yan
2018 arXiv   pre-print
The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search.  ...  We consider the problem of retrieving and ranking items in an eCommerce catalog, often called SKUs, in order of relevance to a user-issued query.  ...  ACKNOWLEDGMENTS The authors would like to thank Ke Shen for his assistance setting up the data collection pipelines.  ... 
arXiv:1806.07296v1 fatcat:g74nbtw7c5eczgemgkwgxdmpzq
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