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Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification [article]

Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
2021 arXiv   pre-print
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario.  ...  The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting.  ...  In this paper, we show that deep graph networks suffer from catastrophic forgetting in class-incremental settings.  ... 
arXiv:2103.11750v1 fatcat:rgzetw7advf53oitqtpgdwquhm

Intelligence at the Extreme Edge: A Survey on Reformable TinyML [article]

Visal Rajapakse, Ishan Karunanayake, Nadeem Ahmed
2022 arXiv   pre-print
In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools.  ...  Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability.  ...  By doing so, the authors effectively delay the effects of Catastrophic Forgetting (discussed further in Section 6.1.1) Network Reliant Approaches IoT, by definition, is a network of interconnected objects  ... 
arXiv:2204.00827v1 fatcat:6wgnjixzvrdg7hfllxyajestra

Social Networks and the Semantic Web

Peter Mika
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
Our benchmark survey data also allows a direct comparison of methods for social network mining.  ...  Scott provides a shorter, but more accessible introductory text on network analysis [Scott, 2000 ].  ...  An eyeball replacement cycle disrupted the intensification, but caused the storm to nearly double in size.  ... 
doi:10.1145/2740908.2742138 dblp:conf/www/Mika15 fatcat:ptyntdmnjrc4tcayyytykwlaba

Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

Kukjin Choi, Jihun Yi, Changhwa Park, Sungroh Yoon
2021 IEEE Access  
Also, we comparatively analyze state-of-the-art deep-anomaly-detection models for time series with several benchmark datasets.  ...  Recent deep learning-based works have made impressive progress in this field.  ...  Common approaches to mitigating catastrophic forgetting include regularization-, dynamic network architectures-, and memory replay-based methods.  ... 
doi:10.1109/access.2021.3107975 fatcat:yrlegcnsy5d47ds3vgbzq64qcu

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey [article]

Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels (+34 others)
2022 arXiv   pre-print
This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge.  ...  However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training.  ...  They alleviate catastrophic forgetting during the training by directly manipulating the gradient for the weight update. In this chapter, we are focusing on Bayesian and gradient based approaches.  ... 
arXiv:2205.04712v1 fatcat:u2bgxr2ctnfdjcdbruzrtjwot4

A Risk Perception Primer: A Narrative Research Review of the Risk Perception Literature in Behavioral Accounting and Behavioral Finance

Victor Ricciardi
2004 Social Science Research Network  
The author provides an overview of the concepts of risk, perception, and risk perception with the financial scholar in mind.  ...  There is also a presentation on the behavioral finance concepts and themes that might influence an individual's perception of risk for different types of financial services and investment products.  ...  an introductory psychology course for two main studies.  ... 
doi:10.2139/ssrn.566802 fatcat:26m3vfy5jbfpva62auiakkacvi

A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models [article]

Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song
2022 arXiv   pre-print
However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed.  ...  It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications.  ...  Avoiding catastrophic forgetting while maintaining the topological structure of the graph, the model achieves the SOTA performance on two AMR-to-text benchmarks.  ... 
arXiv:2201.05337v1 fatcat:lqr6ulndhrcjbiy7etejwtdghy

Artificial Neurogenesis: An Introduction and Selective Review [chapter]

Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier, René Doursat
2014 Studies in Computational Intelligence  
For instance, artificial neural networks are prone to a phenomenon known as "catastrophic forgetting", that is, a tendency to rapidly forget all previously learned knowledge when presented with new data  ...  Neuroevolution In evolutionary computation, there has been an long-standing interest in artificial neural networks for classification and regression, as well as control problems.  ... 
doi:10.1007/978-3-642-55337-0_1 fatcat:xx6nzfvbmfgzjhse6t5il3lbxe

Visions of Globalization: Inequality and Political Stability

Joshua M. Pryor
2013 Social Science Research Network  
In 1970-74, China is in the periphery, located in the north of Graph 3.2, whereas in Graph 3.3 China is located in the middle of the core and cannot be seen on the graph, though it is ranked second on  ...  First, for a baseline, a scatter plot between regime change and repression for only dictatorships is presented in graph 2.3.  ...  or pay in kind" for work in an agricultural sector.  ... 
doi:10.2139/ssrn.2314781 fatcat:ommgwlu3o5d4tpafsjlsydlg6y

Curing and Preventing Euroarea's Sovereign Debt Crises: Some Issues and a Recipe

Franco Bruni
2012 Social Science Research Network  
Special thanks to Shahin Kamalodin. 1 The author is grateful to the Wharton Financial Institutions Center for the opportunity to conduct this research.  ...  Therefore, we include dummy=1 for the existence of an explicit insurance deposit network (see table 1 in the Appendix for details).  ...  the rest of the paper for the graphs and the estimations of our results.  ... 
doi:10.2139/ssrn.2160737 fatcat:lq63beqksbgrnbxzbj3wc56zgi

Patterns, predictions, and actions: A story about machine learning [article]

Moritz Hardt, Benjamin Recht
2021 arXiv   pre-print
A chapter on datasets as benchmarks examines their histories and scientific bases.  ...  This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions.  ...  , Sutskever, and Hinton, "ImageNet Classification with Deep Convolutional Neural Networks." 156 Malik, "What Led Computer Vision to Deep Learning?"  ... 
arXiv:2102.05242v2 fatcat:wy47g4fojnfuxngklyewtjtqdi

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4598227 fatcat:hm2ksetmsvf37adjjefmmbakvq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4591029 fatcat:zn2hvfyupvdwlnvsscdgswayci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4413249 fatcat:35qbhenysfhvza2roihx52afuy
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