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Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
[article]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2015
Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion
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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
2021
IEEE Access
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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]
2022
arXiv
pre-print
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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
2004
Social Science Research Network
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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]
2022
arXiv
pre-print
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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]
2014
Studies in Computational Intelligence
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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
2013
Social Science Research Network
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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
2012
Social Science Research Network
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2102.05242v1 fatcat:ghg6hn4djvdszkwpkg37lul5nm
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
2020
Zenodo
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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
2020
Zenodo
Preserved Fulltext
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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
2020
Zenodo
Preserved Fulltext
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The file type is application/pdf.
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
2020
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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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