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1calico/scBasset: Sequence-based Modeling of single-cell ATAC-seq using Convolutional Neural Networks.
https://github.com/calico/scBasset
2 years ago by @becker
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convolutional
code
cnn
github
neural
atac
network
atacseq
classification
seq
convolutionalcodecnngithubneuralatacnetworkatacseqclassificationseq
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1dmir_wue / code-2020-litgenre — Bitbucket
https://bitbucket.org/dmir_wue/code-2020-litgenre/commits/
3 years ago by @schwemmlein
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literature
nlp
code
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classification
repository
literaturenlpcodegenreclassificationrepository
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1References for literary genre + emotional plot development
https://scholar.google.de/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=16340352417931444070&scipsc=&q=&scisbd=1
3 years ago by @schwemmlein
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literature
nlp
emotion
plot
genre
text
classification
literaturenlpemotionplotgenretextclassification
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1leftthomas/DGCNN: A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"
https://github.com/leftthomas/DGCNN
4 years ago by @becker
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cloud
point
code
cnn
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hithub
python
pytorch
torch
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cloudpointcodecnnimplementationhithubpythonpytorchtorchgraphdgcnnclassification
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1diningphil/gnn-comparison: Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020
https://github.com/diningphil/gnn-comparison
4 years ago by @becker
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comparison
paper
neural
network
graph
classification
comparisonpaperneuralnetworkgraphclassification
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1EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification - Digitale Bibliothek - Gesellschaft für Informatik e.V.
https://dl.gi.de/handle/20.500.12116/24971
5 years ago by @schwemmlein
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myown
embeddings
relation
eclaire
informatik19
word
classification
myownembeddingsrelationeclaireinformatik19wordclassification
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1machine learning - Bias and variance in leave-one-out vs K-fold cross validation - Cross Validated
https://stats.stackexchange.com/questions/61783/bias-and-variance-in-leave-one-out-vs-k-fold-cross-validation
6 years ago by @becker
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classfier
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1predictive models - Differences between cross validation and bootstrapping to estimate the prediction error - Cross Validated
https://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio
6 years ago by @becker
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strapping
evaluation
cross
kfold
prediction
bootstrapping
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classification
validation
strappingevaluationcrosskfoldpredictionbootstrappingbootclassificationvalidation
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1What is the difference between Bagging and Boosting? | Quantdare
https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/
6 years ago by @becker
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bossting
bagging
machine
learning
vs
ml
classification
bosstingbaggingmachinelearningvsmlclassification
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2Classification · fast.ai NLP
http://nlp.fast.ai/category/classification.html
6 years ago by @schwemmlein
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nlp
transfer
language
ulmfit
learning
model
classification
nlptransferlanguageulmfitlearningmodelclassification
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1PAN
PAN is a series of scientific events and shared tasks on digital text forensics and stylometry
6 years ago by @schwemmlein
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task
dataset
text
stylometry
classification
taskdatasettextstylometryclassification
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1GitHub - davidsbatista/Annotated-Semantic-Relationships-Datasets: Public and free annotated datasets of relationships between entities/nominals
https://github.com/davidsbatista/Annotated-Semantic-Relationships-Datasets
6 years ago by @schwemmlein
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set
semantic
data
relation
classification
setsemanticdatarelationclassification
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1Fake News finden und bekämpfen - Fraunhofer SIT
https://www.sit.fraunhofer.de/de/news/aktuelles/presse/details/news-article/fake-news-finden-und-bekaempfen/
6 years ago by @becker
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medwatch
fraunhofer
sit
classification
medwatchfraunhofersitclassification
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1developmentseed/skynet-train: Training and test the SegNet neural network on satellite imagery
https://github.com/developmentseed/skynet-train
6 years ago by @becker
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image
set
skynet
dataset
neural
data
segmentation
satelite
network
classification
imagesetskynetdatasetneuraldatasegmentationsatelitenetworkclassification
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1Performance Evaluation for Learning Algorithms
http://www.site.uottawa.ca/~nat/Talks/ICMLA-Tutorial.pptx
6 years ago by @becker
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slides
statistical
p-value
test
recommendation
learning
classification
p
tests
evaluation
value
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slidesstatisticalp-valuetestrecommendationlearningclassificationptestsevaluationvaluestatisticstatistics
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1Evaluating Learning Algorithms: A Classification Perspective: Amazon.de: Nathalie Japkowicz: Fremdsprachige Bücher
https://www.amazon.de/Evaluating-Learning-Algorithms-Classification-Perspective/dp/1107653118
6 years ago by @becker
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statistical
p-value
book
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p
tests
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value
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statisticalp-valuebooktestrecommendationlearningclassificationptestsevaluationvaluestatisticstatistics
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1CodaLab - Competition
https://competitions.codalab.org/competitions/17422
6 years ago by @schwemmlein
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semantic
task
codalab
2018
semeval
relation
semeval18
classification
semantictaskcodalab2018semevalrelationsemeval18classification
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1SemEval 2018 Task 7 data
http://lipn.univ-paris13.fr/~gabor/semeval2018task7/
6 years ago by @schwemmlein
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semantic
2018
semeval
data
relation
semeval18
results
classification
semantic2018semevaldatarelationsemeval18resultsclassification
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1SemEval-2018 workshop
http://alt.qcri.org/semeval2018/index.php?id=papers
7 years ago by @schwemmlein
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semantic
2018
semeval
workshop
relation
semeval18
naacl
classification
semantic2018semevalworkshoprelationsemeval18naaclclassification
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1yoonkim/CNN_sentence: CNNs for sentence classification
https://github.com/yoonkim/CNN_sentence
7 years ago by @thoni
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sentence
embeddings
classification
sentenceembeddingsclassification
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publications (hide)181
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2Explainable classification of brain networks via contrast subgraphs
T. Lanciano, F. Bonchi, and A. Gionis. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 3308--3318. (2020)
3 years ago by @becker
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disruption
subgraphs
learning
kdd
explainable
networks
citedby:scholar:timestamp:2021-8-8
classification
graphs
contrast
citedby:scholar:count:4
correlation
machine
interpretable
disruptionsubgraphslearningkddexplainablenetworkscitedby:scholar:timestamp:2021-8-8classificationgraphscontrastcitedby:scholar:count:4correlationmachineinterpretable
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2BoostEMM-Transparent Boosting using Exceptional Model Mining.
S. van der Zon, O. Zeev-Ben-Mordehai, T. Vrijdag, W. van Ipenburg, W. Duivesteijn, J. Veldsink, and M. Pechenizkiy. MIDAS@ PKDD/ECML, page 5--16. (2017)
3 years ago by @becker
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subgroup
exceptional
mining
emm
boosting
discovery
prediction
model
boost
classification
subgroupexceptionalminingemmboostingdiscoverypredictionmodelboostclassification
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1Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
S. Sagawa, P. Koh, T. Hashimoto, and P. Liang. International Conference on Learning Representations (ICLR), (2020)
3 years ago by @becker
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project:bmbf
subgroup
subgroups
known
neural
distribution
relatedwork
network
classification
p21
project:bmbfsubgroupsubgroupsknownneuraldistributionrelatedworknetworkclassificationp21
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1No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
N. Sohoni, J. Dunnmon, G. Angus, A. Gu, and C. Ré. Advances in Neural Information Processing Systems, (2020)
4 years ago by @becker
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project:bmbf
subgroup
robustness
discovery
classification
p21
subgroups
citedby:scholar:count:2
subclass
george
relatedwork
citedby:scholar:timestamp:2021-1-16
fariness
project:bmbfsubgrouprobustnessdiscoveryclassificationp21subgroupscitedby:scholar:count:2subclassgeorgerelatedworkcitedby:scholar:timestamp:2021-1-16fariness
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1Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. Sarma, M. Bronstein, and J. Solomon. Acm Transactions On Graphics (tog), 38 (5): 1--12 (2019)
4 years ago by @becker
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cnn
neural
network
graph
classification
cell
citedby:scholar:count:674
point
cloud
citedby:scholar:timestamp:2020-9-10
dynamic
nn
single
cnnneuralnetworkgraphclassificationcellcitedby:scholar:count:674pointcloudcitedby:scholar:timestamp:2020-9-10dynamicnnsingle
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2Analysis of Telegram, An Instant Messaging Service
A. Dargahi Nobari, N. Reshadatmand, and M. Neshati. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, page 2035–2038. New York, NY, USA, Association for Computing Machinery, (2017)
4 years ago by @schwemmlein
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detection
telegram
messaging
antrag
deconspire
analysis
network
pagerank
spam
graph
instant
classification
detectiontelegrammessagingantragdeconspireanalysisnetworkpagerankspamgraphinstantclassification
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3Deep learning for time series classification: a review
H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller. Data Mining and Knowledge Discovery, 33 (4): 917--963 (Jul 1, 2019)
4 years ago by @becker
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cnn
deep
neural
series
learning
time
networks
nn
classification
cnndeepneuralserieslearningtimenetworksnnclassification
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2Linked Document Embedding for Classification
S. Wang, J. Tang, C. Aggarwal, and H. Liu. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM, (October 2016)
4 years ago by @schwemmlein
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nlp
genre2020
document
embeddings
linked
classification
nlpgenre2020documentembeddingslinkedclassification
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2A fair comparison of graph neural networks for graph classification
F. Errica, M. Podda, D. Bacciu, and A. Micheli. arXiv preprint arXiv:1912.09893, (2019)
4 years ago by @becker
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comparison
paper
neural
citedby:scholar:timestamp:2020-6-3
citedby:scholar:count:9
network
graph
classification
comparisonpaperneuralcitedby:scholar:timestamp:2020-6-3citedby:scholar:count:9networkgraphclassification
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2Fusing Transformer Model with Temporal Features for ECG Heartbeat Classification
G. Yan, S. Liang, Y. Zhang, and F. Liu. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), page 898-905. (November 2019)
4 years ago by @nosebrain
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heartbeat
ecg
transformer
classification
heartbeatecgtransformerclassification
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2Deep Learning-based Approach for Atrial Fibrillation Detection
L. Khriji, M. Fradi, M. Machhout, and A. Hossen. EasyChair Preprint no. 2838, (EasyChair, 2020)
4 years ago by @nosebrain
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fcn
deep
ecg
learning
atrial
fibrillation
classification
fcndeepecglearningatrialfibrillationclassification
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2ECGDeepNET: A Deep Learning approach for classifying ECG beats.
T. Mahmud, A. Hossain, and S. Fattah. RiTA, page 32-37. IEEE, (2019)
4 years ago by @nosebrain
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convnet
deep
arrhythmia
ecg
learning
classification
convnetdeeparrhythmiaecglearningclassification
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2Arrhythmia Detection in ECG Signals Using a Multilayer Perceptron Network.
G. Kumar, U. Pawar, and R. O'Reilly. AICS, volume 2563 of CEUR Workshop Proceedings, page 353-364. CEUR-WS.org, (2019)
4 years ago by @nosebrain
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deep
perceptron
arrhythmia
ecg
learning
classification
deepperceptronarrhythmiaecglearningclassification
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2ECG Heartbeat Classification: A Deep Transferable Representation
M. Kachuee, S. Fazeli, and M. Sarrafzadeh. (2018)cite arxiv:1805.00794.
4 years ago by @nosebrain
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heartbeat
ecg
classification
heartbeatecgclassification
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1A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
M. Alfaras, M. Soriano, and S. Ort\'ın. Frontiers in Physics, (July 2019)
4 years ago by @nosebrain
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heartbeat
ecg
ml
classification
heartbeatecgmlclassification
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1ECG multi-class classification using neural network as machine learning model
H. Lassoued, and R. Ketata. 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), page 473-478. (March 2018)
4 years ago by @nosebrain
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multi-class
neural
ecg
network
classification
multi-classneuralecgnetworkclassification
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3Explaining Data-driven Document Classifications
D. Martens, and F. Provost. MIS Q., 38 (1): 73--100 (March 2014)
5 years ago by @schwemmlein
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mining
comprehensibility
document
text
explanation
XAI
classification
miningcomprehensibilitydocumenttextexplanationXAIclassification
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1Correlation dimension-based classifier
M. Jirina, and M. Jirina. IEEE Trans Cybern, 44 (12): 2253-2263 (December 2014)
5 years ago by @becker
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nalab
disruption
cordis
correlation
classifier
chaos
classification
theory
nalabdisruptioncordiscorrelationclassifierchaosclassificationtheory
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8EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification
L. Hettinger, A. Zehe, A. Dallmann, and A. Hotho. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, page 191-204. Bonn, Gesellschaft für Informatik e.V., (2019)
5 years ago by @hotho
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myown
2019
semeval10
word_embedding
embeddings
relation
semeval18
word
classification
myown2019semeval10word_embeddingembeddingsrelationsemeval18wordclassification
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8EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification
L. Hettinger, A. Zehe, A. Dallmann, and A. Hotho. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, page 191-204. Bonn, Gesellschaft für Informatik e.V., (2019)
5 years ago by @schwemmlein
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myown
semeval10
word_embedding
embeddings
relation
semeval18
word
classification
myownsemeval10word_embeddingembeddingsrelationsemeval18wordclassification
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