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1Chen-Cai-OSU/awesome-equivariant-network: Paper list for equivariant neural network
https://github.com/Chen-Cai-OSU/awesome-equivariant-network
2 лет назад , @becker
group
equivariant
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
3 лет назад , @becker
data
dataset
graph
representation
learning
biomedical
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graphs
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1optimization - What causes sudden drops in training/test errors when training a neural network? - Cross Validated
https://stats.stackexchange.com/questions/300839/what-causes-sudden-drops-in-training-test-errors-when-training-a-neural-network
4 лет назад , @becker
learning
rate
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1deep learning - What should I do when my neural network doesn't learn? - Cross Validated
https://stats.stackexchange.com/questions/352036/what-should-i-do-when-my-neural-network-doesnt-learn
4 лет назад , @becker
nn
neural
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stackoverflow
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1A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
4 лет назад , @becker
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1A tutorial on regularized partial correlation networks.
S. Epskamp, и E. Fried. Psychological methods, 23 (4): 617 (2018)
2 лет назад , @becker
paper:fastcor
partial
correlation
network
networks
citedby:scholar:count:1004
citedby:scholar:timestamp:2022-9-10
paper:fastcorpartialcorrelationnetworknetworkscitedby:scholar:count:1004citedby:scholar:timestamp:2022-9-10
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1Genetic and environmental perturbations lead to regulatory decoherence
A. Lea, M. Subramaniam, A. Ko, T. Lehtimäki, E. Raitoharju, M. Kähönen, I. Seppälä, N. Mononen, O. Raitakari, M. Ala-Korpela и 1 other автор(ы). Elife, (2019)
2 лет назад , @becker
cordis
differential
correlation
network
networks
coherence
decoherence
genetic
gene
regulatory
citedby:scholar:count:28
citedby:scholar:timestamp:2022-7-28
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1Integrated intra-and intercellular signaling knowledge for multicellular omics analysis
D. Türei, A. Valdeolivas, L. Gul, N. Palacio-Escat, M. Klein, O. Ivanova, M. Ölbei, A. Gábor, F. Theis, D. Módos и 1 other автор(ы). Molecular systems biology, 17 (3): e9923 (2021)
2 лет назад , @becker
background
cell
cells
citedby:scholar:count:35
citedby:scholar:timestamp:2022-2-23
cytokines
database
immune
knowledge
mining
network
networks
project:iti-single-cell
pubmed
system
text
backgroundcellcellscitedby:scholar:count:35citedby:scholar:timestamp:2022-2-23cytokinesdatabaseimmuneknowledgeminingnetworknetworksproject:iti-single-cellpubmedsystemtext
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1Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed
K. Kveler, E. Starosvetsky, A. Ziv-Kenet, Y. Kalugny, Y. Gorelik, G. Shalev-Malul, N. Aizenbud-Reshef, T. Dubovik, M. Briller, J. Campbell и 1 other автор(ы). Nature biotechnology, 36 (7): 651--659 (2018)
2 лет назад , @becker
background
cell
cells
citedby:scholar:count:51
citedby:scholar:timestamp:2022-2-23
cytokines
database
immune
knowledge
mining
network
networks
project:iti-single-cell
pubmed
system
text
backgroundcellcellscitedby:scholar:count:51citedby:scholar:timestamp:2022-2-23cytokinesdatabaseimmuneknowledgeminingnetworknetworksproject:iti-single-cellpubmedsystemtext
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2CorDiffViz: an R package for visualizing multi-omics differential correlation networks
S. Yu, M. Drton, D. Promislow, и A. Shojaie. BMC bioinformatics, 22 (1): 1--12 (2021)
3 лет назад , @becker
correlation
difference
differential
visualization
network
networks
fastcor
paper:fastcor
correlationdifferencedifferentialvisualizationnetworknetworksfastcorpaper:fastcor
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3Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, и T. Aila. arXiv preprint arXiv:2006.06676, (2020)
3 лет назад , @becker
training
generative
adversarial
networks
gan
limited
data
small
datasets
set
model
neural
network
deep
learning
citedby:scholar:count:128
citedby:scholar:timestamp:2021-7-20
traininggenerativeadversarialnetworksganlimiteddatasmalldatasetssetmodelneuralnetworkdeeplearningcitedby:scholar:count:128citedby:scholar:timestamp:2021-7-20
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2Sampling from social networks with attributes
C. Wagner, P. Singer, F. Karimi, J. Pfeffer, и M. Strohmaier. Proceedings of the 26th international conference on world wide web, стр. 1181--1190. (2017)
3 лет назад , @becker
sampling
networks
attributes
social
network
attribute
citedby:scholar:count:23
citedby:scholar:timestamp:2021-7-1
samplingnetworksattributessocialnetworkattributecitedby:scholar:count:23citedby:scholar:timestamp:2021-7-1
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1Generalized hypergeometric ensembles: Statistical hypothesis testing in complex networks
G. Casiraghi, V. Nanumyan, I. Scholtes, и F. Schweitzer. arXiv preprint arXiv:1607.02441, (2016)
3 лет назад , @becker
citedby:scholar:count:15
citedby:scholar:timestamp:2021-4-15
ensemble
geometric
graph
graphs
hyper
hyptrails
network
networks
citedby:scholar:count:15citedby:scholar:timestamp:2021-4-15ensemblegeometricgraphgraphshyperhyptrailsnetworknetworks
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1Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer
H. Chereda, A. Bleckmann, K. Menck, J. Perera-Bel, P. Stegmaier, F. Auer, F. Kramer, A. Leha, и T. Beißbarth. BioRxiv, (2020)
3 лет назад , @becker
p21
graph
neural
network
networks
relatedwork
citedby:scholar:count:1
citedby:scholar:timestamp:2021-2-14
p21graphneuralnetworknetworksrelatedworkcitedby:scholar:count:1citedby:scholar:timestamp:2021-2-14
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3Higher-order organization of complex networks
A. Benson, D. Gleich, и J. Leskovec. Science, 353 (6295): 163--166 (2016)
4 лет назад , @becker
application
imported
network
networks
paper:fastcor
applicationimportednetworknetworkspaper:fastcor
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2Using Cliques with Higher-Order Spectral Embeddings Improves Graph Visualizations
H. Nassar, C. Kennedy, S. Jain, A. Benson, и D. Gleich. Proceedings of The Web Conference 2020, стр. 2927-2933. New York, NY, USA, Association for Computing Machinery, (2020)
4 лет назад , @becker
application
imported
network
networks
paper:fastcor
applicationimportednetworknetworkspaper:fastcor
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2Relational inductive biases, deep learning, and graph networks
P. Battaglia, J. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner и 1 other автор(ы). arXiv preprint arXiv:1806.01261, (2018)
4 лет назад , @becker
inductive
bias
bayes
bayesian
neural
network
deep
learning
relational
graph
networks
citedby:scholar:count:761
citedby:scholar:timestamp:2020-9-18
inductivebiasbayesbayesianneuralnetworkdeeplearningrelationalgraphnetworkscitedby:scholar:count:761citedby:scholar:timestamp:2020-9-18
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1Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues
C. Lopes-Ramos, C. Chen, M. Kuijjer, J. Paulson, A. Sonawane, M. Fagny, J. Platig, K. Glass, J. Quackenbush, и D. DeMeo. Cell Reports, 31 (12): 107795 (2020)
4 лет назад , @becker
correlation
difference
disruption
estimation
expression
gene
graph
human
lioness
network
networks
paper:cordis
regulatory
sample
sex
single
tissue
correlationdifferencedisruptionestimationexpressiongenegraphhumanlionessnetworknetworkspaper:cordisregulatorysamplesexsingletissue
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1Personalized characterization of diseases using sample-specific networks
X. Liu, Y. Wang, H. Ji, K. Aihara, и L. Chen. Nucleic acids research, 44 (22): e164--e164 (2016)
4 лет назад , @becker
correlation
disruption
single
sample
network
networks
graph
individual
estimation
perturbation
citedby:scholar:count:100
citedby:scholar:timestamp:2020-7-27
correlationdisruptionsinglesamplenetworknetworksgraphindividualestimationperturbationcitedby:scholar:count:100citedby:scholar:timestamp:2020-7-27
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11MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, и A. Hotho. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6 (3): 1--24 (2020)
4 лет назад , @becker
air
cnn
image
map
mapping
myown
network
networks
neural
open
p21
prediction
project:bmbf
quality
street
aircnnimagemapmappingmyownnetworknetworksneuralopenp21predictionproject:bmbfqualitystreet
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2A survey of transfer learning
K. Weiss, T. Khoshgoftaar, и D. Wang. Journal of Big data, 3 (1): 9 (2016)
5 лет назад , @becker
adaption
deep
domain
learning
nalab
network
networks
neural
p21
project:bmbf
relatedwork
review
survey
transfer
adaptiondeepdomainlearningnalabnetworknetworksneuralp21project:bmbfrelatedworkreviewsurveytransfer
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1A survey on transfer learning
S. Pan, и Q. Yang. IEEE Transactions on knowledge and data engineering, 22 (10): 1345--1359 (2009)
5 лет назад , @becker
adaption
deep
domain
learning
nalab
network
networks
neural
review
survey
transfer
adaptiondeepdomainlearningnalabnetworknetworksneuralreviewsurveytransfer
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3A survey on deep transfer learning
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, и C. Liu. International conference on artificial neural networks, стр. 270--279. Springer, (2018)
5 лет назад , @becker
adaption
deep
domain
learning
nalab
network
networks
neural
project:bmbf
review
survey
transfer
adaptiondeepdomainlearningnalabnetworknetworksneuralproject:bmbfreviewsurveytransfer
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1Neural networks for classification: a survey
G. Zhang. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30 (4): 451--462 (2000)
6 лет назад , @becker
deepscan
neural
networks
nn
survey
review
classification
network
citedby:scholar:count:1502
citedby:scholar:timestamp:2018-5-15
deepscanneuralnetworksnnsurveyreviewclassificationnetworkcitedby:scholar:count:1502citedby:scholar:timestamp:2018-5-15
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1Markov random fields and their applications
R. Kindermann, и L. Snell. American Mathematical Society, (1980)
7 лет назад , @becker
chain
citedby:scholar:count:1481
citedby:scholar:timestamp:2017-4-7
diss
field
fields
inthesis
markov
network
networks
random
chaincitedby:scholar:count:1481citedby:scholar:timestamp:2017-4-7dissfieldfieldsinthesismarkovnetworknetworksrandom
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1Chen-Cai-OSU/awesome-equivariant-network: Paper list for equivariant neural network

1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube

1optimization - What causes sudden drops in training/test errors when training a neural network? - Cross Validated

1deep learning - What should I do when my neural network doesn't learn? - Cross Validated

1A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

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1A tutorial on regularized partial correlation networks.

1Genetic and environmental perturbations lead to regulatory decoherence

1Integrated intra-and intercellular signaling knowledge for multicellular omics analysis

1Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

2CorDiffViz: an R package for visualizing multi-omics differential correlation networks

3Training generative adversarial networks with limited data

2Sampling from social networks with attributes

1Generalized hypergeometric ensembles: Statistical hypothesis testing in complex networks

1Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

3Higher-order organization of complex networks

2Using Cliques with Higher-Order Spectral Embeddings Improves Graph Visualizations

2Relational inductive biases, deep learning, and graph networks

1Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

1Personalized characterization of diseases using sample-specific networks

11MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

2A survey of transfer learning

1A survey on transfer learning

3A survey on deep transfer learning

1Neural networks for classification: a survey

1Markov random fields and their applications

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