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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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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
implementation
hithub
python
pytorch
torch
graph
dgcnn
classification
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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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
5 years ago by @becker
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fold
classfier
variance
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bias
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leave
kfold
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foldclassfiervarianceleaveoneoutonebiasevaluationleavekfoldpredictionoutclassification
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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
5 years ago by @becker
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strapping
evaluation
cross
kfold
prediction
bootstrapping
boot
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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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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statistics
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
test
recommendation
learning
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p
tests
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statisticalp-valuebooktestrecommendationlearningclassificationptestsevaluationvaluestatisticstatistics
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1OxfordML/heatmap_expts
https://github.com/OxfordML/heatmap_expts
7 years ago by @becker
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bayesian
ecml
2017
bayes
hetmap
map
heat
classification
bayesianecml2017bayeshetmapmapheatclassification
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2Multiclass classification and pre-trained word embedding (word2vec & GloVe) support and it's comparison · Issue #69 · dennybritz/cnn-text-classification-tf
cnn-text-classification-tf - Convolutional Neural Network for Text Classification in Tensorflow
7 years ago by @hotho
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text
can
tc
classification
textcantcclassification
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3patrickzib/SFA: Scalable Time Series Data Analytics
https://github.com/patrickzib/SFA
7 years ago by @becker
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fourier
of
bag
series
words
time
classification
fourierofbagserieswordstimeclassification
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3Unsupervised sentiment neuron
We’ve developed an unsupervised system which learns an excellent representation of sentiment.
7 years ago by @hotho
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sentiment
character
prediction
vector
classification
sentimentcharacterpredictionvectorclassification
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2GitHub - millecker/senti-storm: SentiStorm - Real-time Twitter Sentiment Classification based on Apache Storm
senti-storm - SentiStorm - Real-time Twitter Sentiment Classification based on Apache Storm
8 years ago by @hotho
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sentiment
storm
toread
twitter
real
time
sentistorm
classification
sentimentstormtoreadtwitterrealtimesentistormclassification
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1DDC Table 8 Genres
http://www.cbtbc.org/table8.php
9 years ago by @becker
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humanities
genre
digital
classification
humanitiesgenredigitalclassification
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2How Amazon.com’s genre classification system affects the discoverability and sales of novels in an online environment | Stephanie Haggarty
https://stephaniehaggarty.wordpress.com/essays/how-amazon-coms-genre-classification-system-affects-the-discoverability-and-sales-of-novels-in-an-online-environment/
9 years ago by @becker
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humanities
amazon
genre
digital
classification
humanitiesamazongenredigitalclassification
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1Brown Corpus Manual
http://clu.uni.no/icame/brown/bcm.html
9 years ago by @becker
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set
humanities
dataset
data
genre
digital
classification
sethumanitiesdatasetdatagenredigitalclassification
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1GFKL
http://www.gfkl2012.de/
12 years ago by @hotho
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pc
conference
2012
gfkl
classification
pcconference2012gfklclassification
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1Large-Scale Hierarchical Classification Workshop | Large Scale Hierarchical Text classification
http://lshtc.iit.demokritos.gr/workshop
14 years ago by @hotho
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pc
workshop
2010
classification
pcworkshop2010classification
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publications (hide)81
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3Explainable 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)
3 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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2Dynamic 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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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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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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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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3Classification Accuracy Score for Conditional Generative Models
S. Ravuri, and O. Vinyals. (2019)cite arxiv:1905.10887.
5 years ago by @becker
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gan
conditional
vae
afcs
model
generative
classification
ganconditionalvaeafcsmodelgenerativeclassification
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5Understanding Where Your Classifier Does (Not) Work--The SCaPE Model Class for EMM
W. Duivesteijn, and J. Thaele. 2014 IEEE International Conference on Data Mining, page 809--814. IEEE, (2014)
5 years ago by @becker
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wouter
project:bmbf
subgroup
subgroups
mining
work
pattern
discovery
classifier
prediction
understand
classification
wouterproject:bmbfsubgroupsubgroupsminingworkpatterndiscoveryclassifierpredictionunderstandclassification
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6Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters
E. Kim, and R. Klinger. (2019)cite arxiv:1903.12453Comment: Accepted for publication at NAACL 2019.
5 years ago by @hotho
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emotion
toread
relationship
classification
emotiontoreadrelationshipclassification
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6A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels
A. Zehe, D. Schlör, U. Henny-Krahmer, M. Becker, and A. Hotho. DH, ADHO, (2018)
5 years ago by @hotho
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myown
2018
nationality
author
cligs
classification
myown2018nationalityauthorcligsclassification
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3Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data
M. Kibanov, M. Becker, J. Müller, M. Atzmueller, A. Hotho, and G. Stumme. Proc. 33rd ACM Symposium On Applied Computing, New York, NY, USA, ACM Press, (2018)
6 years ago by @hotho
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geo
myown
2018
knn
spatial
classification
geomyown2018knnspatialclassification
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7ClaiRE at SemEval-2018 Task 7 - Extended Version
L. Hettinger, A. Dallmann, A. Zehe, T. Niebler, and A. Hotho. (2018)cite arxiv:1804.05825Comment: This is the extended version for our work: ClaiRE at SemEval-2018 Task 7.
6 years ago by @hotho
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2018
relation
preprint
semantics
mown
classification
2018relationpreprintsemanticsmownclassification
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8ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings
L. Hettinger, A. Dallmann, A. Zehe, T. Niebler, and A. Hotho. Proceedings of International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, USA, (2018)
6 years ago by @hotho
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myown
semantic
2018
scientific
embeddings
relation
senegal
w2v
word
classification
myownsemantic2018scientificembeddingsrelationsenegalw2vwordclassification
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2Neural 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 years ago by @becker
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citedby:scholar:count:1502
neural
review
survey
deepscan
network
networks
citedby:scholar:timestamp:2018-5-15
nn
classification
citedby:scholar:count:1502neuralreviewsurveydeepscannetworknetworkscitedby:scholar:timestamp:2018-5-15nnclassification
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2Knowledge acquisition from examples via multiple models
P. Domingos. MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, page 98--106. MORGAN KAUFMANN PUBLISHERS, INC., (1997)
6 years ago by @becker
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forest
decision
domingos
tree
rules
essemble
classification
random
citedby:scholar:count:127
learner
adidas
base
citedby:scholar:timestamp:2018-3-8
forestdecisiondomingostreerulesessembleclassificationrandomcitedby:scholar:count:127learneradidasbasecitedby:scholar:timestamp:2018-3-8
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2Explicative Human Activity Recognition using Adaptive Association Rule-Based Classification
M. Atzmueller, N. Hayat, M. Trojahn, and D. Kroll. Proc. IEEE International Conference on Future IoT Technologies, Boston, MA, USA, IEEE Press, (2018)
6 years ago by @atzmueller
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imported
myown
2018
activity
data
discovery
recognition
explicative
analysis
classification
importedmyown2018activitydatadiscoveryrecognitionexplicativeanalysisclassification
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6Bag of Tricks for Efficient Text Classification
A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. (2016)cite arxiv:1607.01759.
7 years ago by @hotho
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fasttext
efficient
text
classification
fasttextefficienttextclassification
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