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1Can A.I. do our literature reviews for us?
https://twitter.com/chris_bail/status/1517158680025870339?t=_qZ0Itw7mqkB2mRc7OAYNw&s=19
2 years ago by @becker
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literature
the
art
of
gpt3
review
immermind
stater
literaturetheartofgpt3reviewimmermindstater
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1Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer | SpringerLink
https://link.springer.com/article/10.1186/s13059-019-1851-8
3 years ago by @becker
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gene
correlation
review
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coexpression
differential
paper:fastcor
dgca
genecorrelationreviewsurveycoexpressiondifferentialpaper:fastcordgca
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1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.
https://github.com/hurcy/awesome-ehr-deeplearning
4 years ago by @becker
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awesome
overview
deep
cureated
data
review
learning
survey
list
ehr
awesomeoverviewdeepcureateddatareviewlearningsurveylistehr
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1PPL APIs
https://colcarroll.github.io/ppl-api/
4 years ago by @becker
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overview
comparison
language
probabilistic
review
ppl
list
programming
overviewcomparisonlanguageprobabilisticreviewppllistprogramming
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1Disentanglement with Variational Autoencoder: A Review
https://towardsdatascience.com/disentanglement-with-variational-autoencoder-a-review-653a891b69bd
4 years ago by @becker
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variational
beta
autoencoder
auto
survery
disentangle
towardsdatascience.com
vae
review
disentanglement
encoder
variationalbetaautoencoderautosurverydisentangletowardsdatascience.comvaereviewdisentanglementencoder
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1PC FAQ – Ecai 2020
http://ecai2020.eu/pc-faq/
4 years ago by @becker
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cirteria
guidelines
review
ecai
helpful
cirteriaguidelinesreviewecaihelpful
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1Journals and Funders Confront Implicit Bias in Peer Review | AAAS - The World's Largest General Scientific Society
https://www.aaas.org/news/journals-and-funders-confront-implicit-bias-peer-review
6 years ago by @becker
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nationality
gender
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review
nationalitygenderpeerbiaspaper:countryreview
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2Amazon review data
http://jmcauley.ucsd.edu/data/amazon/
8 years ago by @nosebrain
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amazon
data
review
datasetamazondatareview
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1The Paper Reviewing Process | How to Do Great Research
https://greatresearch.org/2013/10/18/the-paper-reviewing-process/
8 years ago by @thoni
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howto
seminar
review
howtoseminarreview
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1Average App Store Review Times
http://appreviewtimes.com/
10 years ago by @nosebrain
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average
app
apple
osx
review
time
ios
statistics
averageappappleosxreviewtimeiosstatistics
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4jameda - Arztbewertungen auf Deutschlands größter Arztempfehlung
Arztbewertungen aus ganz Deutschland helfen Ihnen, den passenden Arzt für sich zu finden. Erfahren Sie, bei wem sich andere Patienten gut aufgehoben fühlen.
11 years ago by @nosebrain
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jameda
doctor
germany
arztbewertungen
recommendation
review
jamedadoctorgermanyarztbewertungenrecommendationreview
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1PC Member Login
https://wi-lab.com/cyberchair/2013/icdm13/pcmb/pc_index.php?subarea=DM
11 years ago by @hotho
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icdm
2013
review
icdm2013review
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1/home/lcharlin/school/research/matching_system/reviewer_data/pubs/papers/hotho@cs.uni-kassel.de – File Manager hotho@cs.uni-kassel.de
http://papermatching.cs.toronto.edu/webapp/fm/
11 years ago by @hotho
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ecml
paper
bidding
manager
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pkdd
ecmlpaperbiddingmanagerreviewpkdd
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1Elsevier Editorial SystemTM
Full-Function Web-Enabled Manuscript Submission and Tracking System for Peer Review
11 years ago by @hotho
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editor
area
semantic
journal
web
jws
review
editorareasemanticjournalwebjwsreview
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1Login - ECMLPKDD'13
https://cmt.research.microsoft.com/ECMLPKDD2013/
11 years ago by @hotho
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2013
review
pkdd
ecml2013reviewpkdd
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1RhodeCode
http://rhodecode.org/
12 years ago by @nosebrain
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git
code
system
management
review
mercurial
RhodeCode
permission
search
fulltext
gitcodesystemmanagementreviewmercurialRhodeCodepermissionsearchfulltext
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120th European Conference on Artificial Intelligence
http://ecai2012.confmaster.net/pages/login.php?Conf=ECAI2012
12 years ago by @hotho
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system
review
ecai
systemreviewecai
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1Braun Kombi 51S / 8000: Amazon.de: Elektronik
http://www.amazon.de/Braun-Kombi-51S-8000/dp/B006F4238M
13 years ago by @nosebrain
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amazon
review
fun
amazonreviewfun
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3gerrit - Gerrit Code Review - Google Project Hosting
http://code.google.com/p/gerrit/
13 years ago by @nosebrain
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git
code
review
gerrit
gitcodereviewgerrit
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1Kiln - Mercurial Version Control and Code Review Software from Fog Creek Software
Kiln is a complete source control management system based on Mercurial with tightly integrated code review from Fog Creek Software
13 years ago by @nosebrain
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system
management
review
source
control
kiln
hg
paid
systemmanagementreviewsourcecontrolkilnhgpaid
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9Representation Learning: A Review and New Perspectives
Y. Bengio, A. Courville, and P. Vincent. IEEE Trans. Pattern Anal. Mach. Intell., 35 (8): 1798-1828 (2013)
2 months ago by @hotho
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toread
perspectives
review
learning
representation
toreadperspectivesreviewlearningrepresentation
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2Image segmentation using deep learning: A survey
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos. IEEE transactions on pattern analysis and machine intelligence, (2021)
a year ago by @becker
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image
citedby:scholar:timestamp:2023-1-5
hecodex
review
segmentation
survey
citedby:scholar:count:1330
imagecitedby:scholar:timestamp:2023-1-5hecodexreviewsegmentationsurveycitedby:scholar:count:1330
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2Integrating Machine Learning with Human Knowledge
C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu. iScience, 23 (11): 101656 (2020)
2 years ago by @becker
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background
machine
review
learning
survey
knowledge
integration
backgroundmachinereviewlearningsurveyknowledgeintegration
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250 years of hormonal contraception—time to find out, what it does to our brain
B. Pletzer, and H. Kerschbaum. Frontiers in neuroscience, (2014)
2 years ago by @becker
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m:id:contraception:coordinate:y:-518.6786193847656
neural
citedby:scholar:timestamp:2022-5-5
neuro
m:id:contraception:node
contraception
m:
brain
citedby:scholar:count:107
project:contraception
review
m:id:contraception:coordinate:x:-384.2443389892578
survey
m:id:contraception:coordinate:y:-518.6786193847656neuralcitedby:scholar:timestamp:2022-5-5neurom:id:contraception:nodecontraceptionm:braincitedby:scholar:count:107project:contraceptionreviewm:id:contraception:coordinate:x:-384.2443389892578survey
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1Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System
L. Shenhav, and M. Azad. mSystems, 7 (1): e01132--21 (2022)
2 years ago by @becker
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machine
review
milk
learning
survey
human
computational
machinereviewmilklearningsurveyhumancomputational
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1AI in health and medicine
P. Rajpurkar, E. Chen, O. Banerjee, and E. Topol. Nature Medicine, (2022)
2 years ago by @becker
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citedby:scholar:timestamp:2022-2-9
artificial
citedby:scholar:count:1
nature
machine
ai
review
learning
medicine
survey
health
intelligence
citedby:scholar:timestamp:2022-2-9artificialcitedby:scholar:count:1naturemachineaireviewlearningmedicinesurveyhealthintelligence
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2Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review
Y. Si, J. Du, Z. Li, X. Jiang, T. Miller, F. Wang, W. Zheng, and K. Roberts. Journal of Biomedical Informatics, (2021)
2 years ago by @becker
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citedby:scholar:count:12
deep
records
learning
representation
health
ehr
citedby:scholar:timestamp:2021-12-15
record
review
survey
embedding
electronic
citedby:scholar:count:12deeprecordslearningrepresentationhealthehrcitedby:scholar:timestamp:2021-12-15recordreviewsurveyembeddingelectronic
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1A survey on contrastive self-supervised learning
A. Jaiswal, A. Babu, M. Zadeh, D. Banerjee, and F. Makedon. Technologies, 9 (1): 2 (2021)
3 years ago by @becker
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citedby:scholar:count:67
supervised
contrastive
review
learning
survey
self
citedby:scholar:timestamp:2021-10-17
citedby:scholar:count:67supervisedcontrastivereviewlearningsurveyselfcitedby:scholar:timestamp:2021-10-17
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1Machine learning for perturbational single-cell omics
Y. Ji, M. Lotfollahi, F. Wolf, and F. Theis. Cell Systems, 12 (6): 522--537 (2021)
3 years ago by @becker
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models
modeling
deep
wolf
perturbation
theis
learning
multi
cell
omics
background
machine
multiomics
review
survey
model
knowledge
single
generative
integration
modelsmodelingdeepwolfperturbationtheislearningmulticellomicsbackgroundmachinemultiomicsreviewsurveymodelknowledgesinglegenerativeintegration
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1A Comprehensive Survey on Graph Neural Networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. Yu. IEEE Transactions on Neural Networks and Learning Systems, (2020)
3 years ago by @becker
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project:bmbf
neural
review
networks
graph
paper:fastcor
project:bmbfneuralreviewnetworksgraphpaper:fastcor
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2Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
A. Andoni, and P. Indyk. 2006 47th annual IEEE symposium on foundations of computer science (FOCS'06), page 459--468. IEEE, (2006)
3 years ago by @becker
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approximate
hashing
lsh
sensitive
review
locality
survey
nearest
neighbor
nn
paper:fastcor
approximatehashinglshsensitivereviewlocalitysurveynearestneighbornnpaper:fastcor
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1Multi-omics data integration, interpretation, and its application
I. Subramanian, S. Verma, S. Kumar, A. Jere, and K. Anamika. Bioinformatics and biology insights, (2020)
3 years ago by @becker
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citedby:scholar:timestamp:2021-2-14
paper:milkreview:selected
data
citedby:scholar:count:57
application
multi
paper:fastcor
p21
omics
multiomics
review
survey
relatedwork
integration
citedby:scholar:timestamp:2021-2-14paper:milkreview:selecteddatacitedby:scholar:count:57applicationmultipaper:fastcorp21omicsmultiomicsreviewsurveyrelatedworkintegration
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1Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics
N. Perakakis, A. Yazdani, G. Karniadakis, and C. Mantzoros. Metabolism-Clinical and Experimental, (2018)
4 years ago by @becker
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editorial
omics
methods
citedby:scholar:count:25
machine
multiomics
review
learning
survey
citedby:scholar:timestamp:2020-8-6
descritpion
editorialomicsmethodscitedby:scholar:count:25machinemultiomicsreviewlearningsurveycitedby:scholar:timestamp:2020-8-6descritpion
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2A survey of multi-view machine learning
S. Sun. Neural computing and applications, 23 (7-8): 2031--2038 (2013)
4 years ago by @becker
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multiview
view
citedby:scholar:timestamp:2020-7-29
review
learning
survey
citedby:scholar:count:576
multi
multiviewviewcitedby:scholar:timestamp:2020-7-29reviewlearningsurveycitedby:scholar:count:576multi
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2A review on machine learning principles for multi-view biological data integration
Y. Li, F. Wu, and A. Ngom. Briefings in bioinformatics, 19 (2): 325--340 (2018)
4 years ago by @becker
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biology
multiview
view
citedby:scholar:timestamp:2020-7-29
paper:milkreview:selected
omics
citedby:scholar:count:143
multiomics
review
relatedwork
multi
p21
biologymultiviewviewcitedby:scholar:timestamp:2020-7-29paper:milkreview:selectedomicscitedby:scholar:count:143multiomicsreviewrelatedworkmultip21
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1Systems immunology: just getting started
M. Davis, C. Tato, and D. Furman. Nat Immunol, 18 (7): 725-732 (June 2017)
4 years ago by @becker
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overview
immune
system
systems
review
survey
relatedwork
immunology
p21
overviewimmunesystemsystemsreviewsurveyrelatedworkimmunologyp21
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3A survey of transfer learning
K. Weiss, T. Khoshgoftaar, and D. Wang. Journal of Big data, 3 (1): 9 (2016)
4 years ago by @becker
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project:bmbf
adaption
nalab
deep
neural
learning
network
networks
p21
domain
transfer
review
survey
relatedwork
project:bmbfadaptionnalabdeepneurallearningnetworknetworksp21domaintransferreviewsurveyrelatedwork
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1A survey on transfer learning
S. Pan, and Q. Yang. IEEE Transactions on knowledge and data engineering, 22 (10): 1345--1359 (2009)
4 years ago by @becker
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domain
adaption
nalab
transfer
deep
neural
review
learning
survey
network
networks
domainadaptionnalabtransferdeepneuralreviewlearningsurveynetworknetworks
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4A survey on deep transfer learning
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu. International conference on artificial neural networks, page 270--279. Springer, (2018)
4 years ago by @becker
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domain
project:bmbf
adaption
nalab
transfer
deep
neural
review
learning
survey
network
networks
domainproject:bmbfadaptionnalabtransferdeepneuralreviewlearningsurveynetworknetworks
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3Machine Learning on Human Connectome Data from MRI
C. Brown, and G. Hamarneh. (2016)cite arxiv:1611.08699Comment: 51 pages, 6 figures. To be submitted to a journal.
4 years ago by @becker
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image
nalab
functional
disruption
cordis
learning
images
fmri
brain
correlation
machine
connectome
review
survey
imagenalabfunctionaldisruptioncordislearningimagesfmribraincorrelationmachineconnectomereviewsurvey
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