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1IraKorshunova/bruno: a deep recurrent model for exchangeable data
https://github.com/IraKorshunova/bruno
5 лет назад , @becker
bayesian
gan
deep
neural
bayes
conditional
network
recurrent
nn
generative
bruno
bayesiangandeepneuralbayesconditionalnetworkrecurrentnngenerativebruno
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1CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1 - YouTube
https://www.youtube.com/watch?v=i94OvYb6noo
5 лет назад , @becker
youtube
neural
weight
back
update
learning
video
network
karpathy
propagation
prop
yt
backprop
nn
youtubeneuralweightbackupdatelearningvideonetworkkarpathypropagationpropytbackpropnn
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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
stagnant
drop
neural
learning
decay
rate
network
networks
decrease
nn
stagnantdropneurallearningdecayratenetworknetworksdecreasenn
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2Debugging Neural Networks: A Checklist – Engineering@Semantics3
https://engineering.semantics3.com/debugging-neural-networks-a-checklist-ca52e11151ec
7 лет назад , @becker
debug
optimization
neural
optimize
debugging
network
nn
debugoptimizationneuraloptimizedebuggingnetworknn
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1Neural network configuration
https://twitter.com/karpathy/status/1528808361558306817?t=DcAepTf_tNH1FP-wEjzWzQ&s=19
2 лет назад , @becker
python
neural
network
config
nn
pythonneuralnetworkconfignn
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1Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/
3 лет назад , @becker
gcn
toread
neural
convolution
gnn
network
graph
nn
gcntoreadneuralconvolutiongnnnetworkgraphnn
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2rasbt/python-machine-learning-book-2nd-edition: The "Python Machine Learning (2nd edition)" book code repository and info resource
https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition
7 лет назад , @becker
jupyter
code
neural
book
machine
ipython
tutorial
learning
network
networks
ml
nn
jupytercodeneuralbookmachineipythontutoriallearningnetworknetworksmlnn
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1[1802.07535] BRUNO: A Deep Recurrent Model for Exchangeable Data
https://arxiv.org/abs/1802.07535
5 лет назад , @becker
bayesian
gan
deep
neural
bayes
network
recurrent
nn
bruno
bayesiangandeepneuralbayesnetworkrecurrentnnbruno
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1Airnet: a Machine Learning Dataset for Air Quality Forecasting - Semantic Scholar
https://www.semanticscholar.org/paper/Airnet%3A-a-Machine-Learning-Dataset-for-Air-Quality-XTRACTION/57e9c34b0c0ca1f5510f2be161888730ac67e02e
6 лет назад , @becker
dataset
neural
air
lur
p2map
network
eva
quality
nn
pollution
datasetneuralairlurp2mapnetworkevaqualitynnpollution
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1Neural network AI is simple. So… Stop pretending you are a genius
https://www.kdnuggets.com/2018/02/neural-network-ai-simple-genius.html
6 лет назад , @becker
kdnuggets
neural
kd
critique
nuggets
network
bash
nn
kdnuggetsneuralkdcritiquenuggetsnetworkbashnn
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2Chain of Thought Prompting Elicits Reasoning in Large Language Models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, и D. Zhou. (2022)cite arxiv:2201.11903.
2 лет назад , @becker
nlp
reasoning
large
neural
language
prompting
google
model
network
nn
nlpreasoninglargeneurallanguagepromptinggooglemodelnetworknn
копироватьудалитьдобавить публикацию в буфер
3Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
S. Cuomo, V. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, и F. Piccialli. arXiv preprint arXiv:2201.05624, (2022)
2 лет назад , @becker
equation
pde
citedby:scholar:count:45
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdecitedby:scholar:count:45neuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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1A data-driven health index for neonatal morbidities
D. De Francesco, Y. Blumenfeld, I. Marić, J. Mayo, A. Chang, R. Fallahzadeh, T. Phongpreecha, A. Butwick, M. Xenochristou, C. Phibbs и 11 other автор(ы). iScience, (2022)
2 лет назад , @becker
multitask
myown
neural
records
pregnancy
index
network
health
multi
neonatal
ehr
sequence
task
model
temporal
recurrent
nn
multitaskmyownneuralrecordspregnancyindexnetworkhealthmultineonatalehrsequencetaskmodeltemporalrecurrentnn
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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, и G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
год назад , @becker
comparison
forest
decision
neural
data
tabular
learning
tree
trees
network
performance
random
citedby:scholar:count:5
evaluation
citedby:scholar:timestamp:2022-12-1
machine
ml
nn
comparisonforestdecisionneuraldatatabularlearningtreetreesnetworkperformancerandomcitedby:scholar:count:5evaluationcitedby:scholar:timestamp:2022-12-1machinemlnn
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2Three ways to solve partial differential equations with neural networks—A review
J. Blechschmidt, и O. Ernst. GAMM-Mitteilungen, 44 (2): e202100006 (2021)
2 лет назад , @becker
equation
pde
neural
round1
citedby:scholar:count:48
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdeneuralround1citedby:scholar:count:48partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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2Neural Network Module Decomposition and Recomposition
H. Kingetsu, K. Kobayashi, и T. Suzuki. arXiv preprint arXiv:2112.13208, (2021)
2 лет назад , @becker
modular
citedby:scholar:count:1
neural
bd:RQhRZNaErnB:group:X1test
decomposition
networks
project:functional-subgroups
citedby:scholar:timestamp:2022-3-25
nn
modularcitedby:scholar:count:1neuralbd:RQhRZNaErnB:group:X1testdecompositionnetworksproject:functional-subgroupscitedby:scholar:timestamp:2022-3-25nn
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3Subgroup generalization and fairness of graph neural networks
J. Ma, J. Deng, и Q. Mei. Advances in Neural Information Processing Systems, (2021)
год назад , @becker
fairness
subgroup
subgroups
neural
citedby:scholar:count:22
gnn
neurips
citedby:scholar:timestamp:2022-12-13
network
graph
themis
nn
fairnesssubgroupsubgroupsneuralcitedby:scholar:count:22gnnneuripscitedby:scholar:timestamp:2022-12-13networkgraphthemisnn
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1Longitudinal variational autoencoder
S. Ramchandran, G. Tikhonov, K. Kujanpää, M. Koskinen, и H. Lähdesmäki. International Conference on Artificial Intelligence and Statistics, стр. 3898--3906. PMLR, (2021)
2 лет назад , @becker
variational
autoencoder
deep
data
citedby:scholar:count:6
learning
gain
citedby:scholar:timestamp:2022-5-13
timeseries
imputation
series
time
missing
model
net
temporal
nn
generative
longitudinal
variationalautoencoderdeepdatacitedby:scholar:count:6learninggaincitedby:scholar:timestamp:2022-5-13timeseriesimputationseriestimemissingmodelnettemporalnngenerativelongitudinal
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2DeepXDE: A deep learning library for solving differential equations
L. Lu, X. Meng, Z. Mao, и G. Karniadakis. SIAM Review, 63 (1): 208--228 (2021)
2 лет назад , @becker
equation
pde
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
citedby:scholar:count:574
nn
equationpdeneuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialcitedby:scholar:count:574nn
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2How to represent part-whole hierarchies in a neural network
G. Hinton. arXiv preprint arXiv:2102.12627, (2021)
3 лет назад , @becker
citedby:scholar:timestamp:2021-11-8
glom
hierarchies
neural
hinton
citedby:scholar:count:27
network
vision
nn
citedby:scholar:timestamp:2021-11-8glomhierarchiesneuralhintoncitedby:scholar:count:27networkvisionnn
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2Evolution-Informed Neural Networks for Microbiome Data Analysis
M. Ito, Y. Glaser, и P. Sadowski. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), стр. 3386--3391. IEEE, (2021)
2 лет назад , @becker
deep
background
micribiome
bioinformatics
neutral
learning
knowledge
network
information
themis
nn
deepbackgroundmicribiomebioinformaticsneutrallearningknowledgenetworkinformationthemisnn
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3Measuring model complexity of neural networks with curve activation functions
X. Hu, W. Liu, J. Bian, и J. Pei. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, стр. 1521--1531. (2020)
2 лет назад , @becker
complexity
neural
citedby:scholar:count:5
model
project:functional-subgroups
networks
citedby:scholar:timestamp:2022-3-25
nn
complexityneuralcitedby:scholar:count:5modelproject:functional-subgroupsnetworkscitedby:scholar:timestamp:2022-3-25nn
копироватьудалитьдобавить публикацию в буфер
3Deep learning for time series classification: a review
H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, и P. Muller. Data Mining and Knowledge Discovery, 33 (4): 917--963 (01.07.2019)
4 лет назад , @becker
cnn
deep
neural
series
learning
time
networks
nn
classification
cnndeepneuralserieslearningtimenetworksnnclassification
копироватьудалитьдобавить публикацию в буфер
2Supertml: Two-dimensional word embedding for the precognition on structured tabular data
B. Sun, L. Yang, W. Zhang, M. Lin, P. Dong, C. Young, и J. Dong. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, стр. 0--0. (2019)
3 лет назад , @becker
citedby:scholar:timestamp:2021-4-24
cnn
neural
data
citedby:scholar:count:6
tabular
network
nn
citedby:scholar:timestamp:2021-4-24cnnneuraldatacitedby:scholar:count:6tabularnetworknn
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2Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, и G. Karniadakis. Journal of Computational physics, (2019)
2 лет назад , @becker
equation
pde
citedby:scholar:count:3855
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdecitedby:scholar:count:3855neuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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2Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. Sarma, M. Bronstein, и J. Solomon. Acm Transactions On Graphics (tog), 38 (5): 1--12 (2019)
4 лет назад , @becker
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
копироватьудалитьдобавить публикацию в буфер
1Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology
Y. Liu, Y. Zhou, X. Liu, F. Dong, C. Wang, и Z. Wang. Engineering, 5 (1): 156 - 163 (2019)
5 лет назад , @becker
gan
neural
adversarial
data
afcs
model
augmentation
network
generative
nn
ganneuraladversarialdataafcsmodelaugmentationnetworkgenerativenn
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2Deep learning for inferring gene relationships from single-cell expression data
Y. Yuan, и Z. Bar-Joseph. Proceedings of the National Academy of Sciences, 116 (52): 27151--27158 (2019)
4 лет назад , @becker
cnn
gene
deep
neural
prediction
learning
network
nn
single
cell
cnngenedeepneuralpredictionlearningnetworknnsinglecell
копироватьудалитьдобавить публикацию в буфер
3Gain: Missing data imputation using generative adversarial nets
J. Yoon, J. Jordon, и M. Schaar. International conference on machine learning, стр. 5689--5698. PMLR, (2018)
2 лет назад , @becker
deep
adversarial
data
learning
gain
citedby:scholar:timestamp:2022-5-13
citedby:scholar:count:468
imputation
missing
model
net
nn
generative
deepadversarialdatalearninggaincitedby:scholar:timestamp:2022-5-13citedby:scholar:count:468imputationmissingmodelnetnngenerative
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2OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
S. Ma, и Z. Zhang. arXiv preprint arXiv:1804.05283, (2018)
4 лет назад , @becker
cnn
convoluational
deep
neural
tree
network
multi
omics
citedby:scholar:count:5
multiomics
citedby:scholar:timestamp:2020-8-31
map
nn
cnnconvoluationaldeepneuraltreenetworkmultiomicscitedby:scholar:count:5multiomicscitedby:scholar:timestamp:2020-8-31mapnn
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BibSonomy

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1IraKorshunova/bruno: a deep recurrent model for exchangeable data

1CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1 - YouTube

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

2Debugging Neural Networks: A Checklist – Engineering@Semantics3

1Neural network configuration

1Understanding Convolutions on Graphs

2rasbt/python-machine-learning-book-2nd-edition: The "Python Machine Learning (2nd edition)" book code repository and info resource

1[1802.07535] BRUNO: A Deep Recurrent Model for Exchangeable Data

1Airnet: a Machine Learning Dataset for Air Quality Forecasting - Semantic Scholar

1Neural network AI is simple. So… Stop pretending you are a genius

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2Chain of Thought Prompting Elicits Reasoning in Large Language Models

3Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

1A data-driven health index for neonatal morbidities

2Why do tree-based models still outperform deep learning on typical tabular data?

2Three ways to solve partial differential equations with neural networks—A review

2Neural Network Module Decomposition and Recomposition

3Subgroup generalization and fairness of graph neural networks

1Longitudinal variational autoencoder

2DeepXDE: A deep learning library for solving differential equations

2How to represent part-whole hierarchies in a neural network

2Evolution-Informed Neural Networks for Microbiome Data Analysis

3Measuring model complexity of neural networks with curve activation functions

3Deep learning for time series classification: a review

2Supertml: Two-dimensional word embedding for the precognition on structured tabular data

2Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

2Dynamic graph cnn for learning on point clouds

1Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

2Deep learning for inferring gene relationships from single-cell expression data

3Gain: Missing data imputation using generative adversarial nets

2OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery

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