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1Tutorial 9: Deep Autoencoders — UvA DL Notebooks v1.2 documentation
https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial9/AE_CIFAR10.html
3 years ago by @becker
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autoencoder
python
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pytorch
dtsclub
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nn
autoencoderpythonneuraltutorialpytorchdtsclubnetworknn
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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
3 years ago by @becker
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equivariant
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equivariantdeepneuralgeometriclearningmmiskirstenetworknetworksgroupnn
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1calico/scBasset: Sequence-based Modeling of single-cell ATAC-seq using Convolutional Neural Networks.
https://github.com/calico/scBasset
3 years ago by @becker
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convolutional
code
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convolutionalcodecnngithubneuralatacnetworkatacseqclassificationseq
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1GitHub - grok-ai/nn-template: Generic template to bootstrap your PyTorch project.
https://github.com/grok-ai/nn-template
4 years ago by @becker
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code
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1Neural network configuration
https://twitter.com/karpathy/status/1528808361558306817?t=DcAepTf_tNH1FP-wEjzWzQ&s=19
4 years ago by @becker
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pythonneuralnetworkconfignn
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5DALL·E 2
https://openai.com/dall-e-2/
4 years ago by @becker
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imagerealisticopenaitoreadneuraldallenetworkgeneration
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1Introducing Neural Structured Learning in TensorFlow | by TensorFlow | TensorFlow | Medium
https://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd
4 years ago by @becker
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1Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/
4 years ago by @becker
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1Explaining and Interpreting LSTMs | SpringerLink
https://link.springer.com/chapter/10.1007/978-3-030-28954-6_11#fromHistory
5 years ago by @schwemmlein
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neuralinterpretationnetworklstmexplanation
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1A Visual Guide to Using BERT for the First Time – Jay Alammar – Visualizing machine learning one concept at a time.
https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
5 years ago by @becker
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1WangYueFt/dgcnn
https://github.com/WangYueFt/dgcnn
5 years ago by @becker
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2Latent graph neural networks: Manifold learning 2.0? | by Michael Bronstein | Sep, 2020 | Towards Data Science
https://towardsdatascience.com/manifold-learning-2-99a25eeb677d
5 years ago by @becker
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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
5 years ago by @becker
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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
5 years ago by @becker
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neuralmistakestrainingnetworkstacknetworksoverflow101crossvalidatedguidestackoverflownn
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2Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science
https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
5 years ago by @becker
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transferdatasetneuralsizeusemachinelearningtonetworkwhenmlnn
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1Breaking the curse of small data sets in Machine Learning: Part 2
https://towardsdatascience.com/breaking-the-curse-of-small-data-sets-in-machine-learning-part-2-894aa45277f4
6 years ago by @becker
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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
6 years ago by @becker
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comparison
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graph
classification
comparisonpaperneuralnetworkgraphclassification
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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
6 years ago by @becker
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transfer
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butterfly
project
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transferdeepneuralbutterflyprojectlearningechonetworknetworksnn
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1What is amortized variational inference? - Quora
https://www.quora.com/What-is-amortized-variational-inference#:~:text=Amortizing%20Posterior%20Inference,of%20the%20approximate%20posterior%20distribution.
6 years ago by @becker
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1https://scholar.googleusercontent.com/scholar.bib?q=info:vegxY8zEn-EJ:scholar.google.com/&output=citation&scisdr=CgUqPzX2ELPhtbXua4o:AAGBfm0AAAAAXlbrc4r8g9aX1vJ_7ANfvGsZjz2luRyR&scisig=AAGBfm0AAAAAXlbrc2pK3JcRojAQtKHudSF69nVueJ6A&scisf=4&ct=citation&cd=0&hl=en
https://scholar.googleusercontent.com/scholar.bib?q=info:vegxY8zEn-EJ:scholar.google.com/&output=citation&scisdr=CgUqPzX2ELPhtbXua4o:AAGBfm0AAAAAXlbrc4r8g9aX1vJ_7ANfvGsZjz2luRyR&scisig=AAGBfm0AAAAAXlbrc2pK3JcRojAQtKHudSF69nVueJ6A&scisf=4&ct=citation&cd=0&hl=en
6 years ago by @becker
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bayesian
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bayesianautoencoderautodeepneuralbayesvaelearningencoderalignednetwork
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2Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer
H. Chereda, A. Bleckmann, F. Kramer, A. Leha, and T. Beissbarth. Studies in health technology and informatics, (September 2019)
2 years ago by @becker
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neural
background
gnn
machine
learning
knowledge
networks
graph
bki
ml
informed
integration
neuralbackgroundgnnmachinelearningknowledgenetworksgraphbkimlinformedintegration
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1Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, and S. Ji. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2022)
3 years ago by @becker
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artificial
neural
citedby:scholar:timestamp:2023-4-9
gnn
ai
explainable
citedby:scholar:count:203
networks
graph
xai
intelligence
artificialneuralcitedby:scholar:timestamp:2023-4-9gnnaiexplainablecitedby:scholar:count:203networksgraphxaiintelligence
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1In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network
R. Fallahzadeh, N. Bidoki, I. Stelzer, M. Becker, I. Marić, A. Chang, A. Culos, T. Phongpreecha, M. Xenochristou, D. De Francesco and 1 other author(s). Cytometry Part A, (2022)
3 years ago by @becker
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high
myown
dimensional
neural
encoder
network
single
cell
highmyowndimensionalneuralencodernetworksinglecell
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3Subgroup generalization and fairness of graph neural networks
J. Ma, J. Deng, and Q. Mei. Advances in Neural Information Processing Systems, (2021)
3 years ago by @becker
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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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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
3 years ago by @becker
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comparison
forest
decision
neural
data
tabular
learning
tree
trees
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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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1Graph representation learning in biomedicine and healthcare
M. Li, K. Huang, and M. Zitnik. Nature Biomedical Engineering, (2022)
3 years ago by @becker
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toread
neural
learning
biomedicine
medicine
representation
networks
graph
health
themis
healthcare
toreadneurallearningbiomedicinemedicinerepresentationnetworksgraphhealththemishealthcare
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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, and G. Karniadakis. Journal of Computational physics, (2019)
3 years ago by @becker
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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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1DeepXDE: A deep learning library for solving differential equations
L. Lu, X. Meng, Z. Mao, and G. Karniadakis. SIAM Review, 63 (1): 208--228 (2021)
3 years ago by @becker
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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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1Three ways to solve partial differential equations with neural networks—A review
J. Blechschmidt, and O. Ernst. GAMM-Mitteilungen, 44 (2): e202100006 (2021)
3 years ago by @becker
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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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4Scientific 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, and F. Piccialli. arXiv preprint arXiv:2201.05624, (2022)
3 years ago by @becker
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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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2A generalization of transformer networks to graphs
V. Dwivedi, and X. Bresson. arXiv preprint arXiv:2012.09699, (2020)
3 years ago by @becker
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citedby:scholar:count:85
generalization
graphs
toread
neural
transformer
citedby:scholar:timestamp:2022-6-29
network
graph
citedby:scholar:count:85generalizationgraphstoreadneuraltransformercitedby:scholar:timestamp:2022-6-29networkgraph
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1Explaining deep neural networks and beyond: A review of methods and applications
W. Samek, G. Montavon, S. Lapuschkin, C. Anders, and K. Müller. Proceedings of the IEEE, 109 (3): 247--278 (2021)
3 years ago by @becker
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neural
citedby:scholar:count:193
machine
citedby:scholar:timestamp:2022-6-27
learning
explainable
networks
themis
neuralcitedby:scholar:count:193machinecitedby:scholar:timestamp:2022-6-27learningexplainablenetworksthemis
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1Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
G. Silvestri, E. Fertig, D. Moore, and L. Ambrogioni. arXiv preprint arXiv:2110.06021, (2021)
4 years ago by @becker
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modeling
deep
neural
probabilistic
learning
network
citedby:scholar:timestamp:2022-5-12
toread
citedby:scholar:count:1
normalizing
programs
layers
flow
programming
modelingdeepneuralprobabilisticlearningnetworkcitedby:scholar:timestamp:2022-5-12toreadcitedby:scholar:count:1normalizingprogramslayersflowprogramming
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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)
4 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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2How oral contraceptives impact social-emotional behavior and brain function
E. Montoya, and P. Bos. Trends in cognitive sciences, 21 (2): 125--136 (2017)
4 years ago by @becker
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social
emotional
neural
citedby:scholar:timestamp:2022-5-5
neuro
m:id:contraception:node
contraception
m:
brain
citedby:scholar:count:102
project:contraception
connectivity
m:id:contraception:coordinate:x:-288.2957534790039
function
oral
m:id:contraception:coordinate:y:-570.919921875
behavior
socialemotionalneuralcitedby:scholar:timestamp:2022-5-5neurom:id:contraception:nodecontraceptionm:braincitedby:scholar:count:102project:contraceptionconnectivitym:id:contraception:coordinate:x:-288.2957534790039functionoralm:id:contraception:coordinate:y:-570.919921875behavior
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2GreaseLM: Graph REASoning Enhanced Language Models
X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. Manning, and J. Leskovec. International Conference on Learning Representations, (2021)
4 years ago by @becker
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nlp
toread
reasoning
neural
language
model
knowledge
network
jure
graph
nlptoreadreasoningneurallanguagemodelknowledgenetworkjuregraph
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1Chain of Thought Prompting Elicits Reasoning in Large Language Models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou. (2022)cite arxiv:2201.11903.
4 years ago by @becker
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nlp
reasoning
large
neural
language
prompting
google
model
network
nn
nlpreasoninglargeneurallanguagepromptinggooglemodelnetworknn
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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 and 11 other author(s). iScience, (2022)
4 years ago by @becker
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multitask
myown
neural
records
pregnancy
index
network
health
multi
neonatal
ehr
sequence
task
model
temporal
recurrent
nn
multitaskmyownneuralrecordspregnancyindexnetworkhealthmultineonatalehrsequencetaskmodeltemporalrecurrentnn
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1Neural Network Module Decomposition and Recomposition
H. Kingetsu, K. Kobayashi, and T. Suzuki. arXiv preprint arXiv:2112.13208, (2021)
4 years ago by @becker
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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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1Modular neural network and self-decomposition
E. Ronco, H. Gollee, and P. Gawthrop. Connection Science (special issue: COMBINING NEURAL NETS).(To appear), (1996)
4 years ago by @becker
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modular
neural
decomposition
networks
project:functional-subgroups
nn
modularneuraldecompositionnetworksproject:functional-subgroupsnn
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