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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
a year ago by @becker
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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
a year ago by @becker
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1Neural network configuration
https://twitter.com/karpathy/status/1528808361558306817?t=DcAepTf_tNH1FP-wEjzWzQ&s=19
2 years ago by @becker
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confignetworkneuralnnpython
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1Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/
3 years ago by @becker
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graphconvolutionneuralnetworknngcngnntoread
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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
4 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
4 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
4 years ago by @becker
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3Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science
https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
4 years ago by @becker
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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
4 years ago by @becker
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2Yes you should understand backprop - Andrej Karpathy - Medium
https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b
5 years ago by @becker
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1CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1 - YouTube
https://www.youtube.com/watch?v=i94OvYb6noo
5 years ago by @becker
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1Bayesian Neural Network Series Post 2: Background Knowledge
https://medium.com/neuralspace/bayesian-neural-network-series-post-2-background-knowledge-fdec6ac62d43
5 years ago by @becker
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1Convolutional neural networks for artistic style transfer — Harish Narayanan
https://harishnarayanan.org/writing/artistic-style-transfer/
5 years ago by @becker
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transfer
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tutorial
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1olofmogren/c-rnn-gan: Implementation of C-RNN-GAN.
https://github.com/olofmogren/c-rnn-gan
5 years ago by @becker
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1IraKorshunova/bruno: a deep recurrent model for exchangeable data
https://github.com/IraKorshunova/bruno
5 years ago by @becker
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1[1802.07535] BRUNO: A Deep Recurrent Model for Exchangeable Data
https://arxiv.org/abs/1802.07535
5 years ago by @becker
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gan
bayes
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2slundberg/shap: A unified approach to explain the output of any machine learning model.
https://github.com/slundberg/shap
5 years ago by @becker
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ml
explainability
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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 years ago by @becker
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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 years ago by @becker
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air
quality
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p2map
nn
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dataset
pollution
lur
airqualityevap2mapnnneuralnetworkdatasetpollutionlur
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2Debugging Neural Networks: A Checklist – Engineering@Semantics3
https://engineering.semantics3.com/debugging-neural-networks-a-checklist-ca52e11151ec
7 years ago by @becker
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debugging
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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)
a year ago by @becker
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neurips
subgroup
subgroups
fairness
graph
neural
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gnn
nn
themis
citedby:scholar:count:22
citedby:scholar:timestamp:2022-12-13
neuripssubgroupsubgroupsfairnessgraphneuralnetworkgnnnnthemiscitedby:scholar:count:22citedby:scholar:timestamp:2022-12-13
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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)
a year ago by @becker
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citedby:scholar:count:5
citedby:scholar:timestamp:2022-12-1
comparison
data
decision
evaluation
forest
learning
machine
ml
network
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nn
performance
random
tabular
tree
trees
citedby:scholar:count:5citedby:scholar:timestamp:2022-12-1comparisondatadecisionevaluationforestlearningmachinemlnetworkneuralnnperformancerandomtabulartreetrees
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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)
2 years ago by @becker
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pde
nn
neural
network
partial
differential
equation
round1
citedby:scholar:count:3855
citedby:scholar:timestamp:2022-11-11
pdennneuralnetworkpartialdifferentialequationround1citedby:scholar:count:3855citedby:scholar:timestamp:2022-11-11
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2DeepXDE: A deep learning library for solving differential equations
L. Lu, X. Meng, Z. Mao, and G. Karniadakis. SIAM Review, 63 (1): 208--228 (2021)
2 years ago by @becker
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nn
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differential
equation
round1
citedby:scholar:count:574
citedby:scholar:timestamp:2022-11-11
pdennneuralnetworkpartialdifferentialequationround1citedby:scholar:count:574citedby:scholar:timestamp:2022-11-11
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2Three ways to solve partial differential equations with neural networks—A review
J. Blechschmidt, and O. Ernst. GAMM-Mitteilungen, 44 (2): e202100006 (2021)
2 years ago by @becker
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pde
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round1
citedby:scholar:count:48
citedby:scholar:timestamp:2022-11-11
pdennneuralnetworkpartialdifferentialequationround1citedby:scholar:count:48citedby:scholar:timestamp:2022-11-11
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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, and F. Piccialli. arXiv preprint arXiv:2201.05624, (2022)
2 years ago by @becker
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equation
round1
citedby:scholar:count:45
citedby:scholar:timestamp:2022-11-11
pdennneuralnetworkpartialdifferentialequationround1citedby:scholar:count:45citedby:scholar:timestamp:2022-11-11
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2Evolution-Informed Neural Networks for Microbiome Data Analysis
M. Ito, Y. Glaser, and P. Sadowski. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), page 3386--3391. IEEE, (2021)
2 years ago by @becker
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background
bioinformatics
deep
information
knowledge
learning
micribiome
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neutral
nn
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backgroundbioinformaticsdeepinformationknowledgelearningmicribiomenetworkneutralnnthemis
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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, and D. Zhou. (2022)cite arxiv:2201.11903.
2 years ago by @becker
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google
language
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googlelanguagelargemodelnetworkneuralnlpnnpromptingreasoning
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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)
2 years ago by @becker
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ehr
health
index
model
multi
multitask
myown
neonatal
network
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nn
pregnancy
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task
temporal
ehrhealthindexmodelmultimultitaskmyownneonatalnetworkneuralnnpregnancyrecordsrecurrentsequencetasktemporal
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2How to represent part-whole hierarchies in a neural network
G. Hinton. arXiv preprint arXiv:2102.12627, (2021)
3 years ago by @becker
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hinton
nn
hierarchies
neural
network
vision
glom
citedby:scholar:count:27
citedby:scholar:timestamp:2021-11-8
hintonnnhierarchiesneuralnetworkvisionglomcitedby:scholar:count:27citedby:scholar:timestamp:2021-11-8
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2Supertml: Two-dimensional word embedding for the precognition on structured tabular data
B. Sun, L. Yang, W. Zhang, M. Lin, P. Dong, C. Young, and J. Dong. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, page 0--0. (2019)
3 years ago by @becker
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citedby:scholar:count:6
citedby:scholar:timestamp:2021-4-24
cnn
data
network
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nn
tabular
citedby:scholar:count:6citedby:scholar:timestamp:2021-4-24cnndatanetworkneuralnntabular
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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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graph
cnn
point
cloud
classification
dynamic
single
cell
neural
network
nn
citedby:scholar:count:674
citedby:scholar:timestamp:2020-9-10
graphcnnpointcloudclassificationdynamicsinglecellneuralnetworknncitedby:scholar:count:674citedby:scholar:timestamp:2020-9-10
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2OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
S. Ma, and Z. Zhang. arXiv preprint arXiv:1804.05283, (2018)
4 years ago by @becker
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convoluational
nn
neural
network
cnn
multi
omics
multiomics
deep
map
tree
citedby:scholar:count:5
citedby:scholar:timestamp:2020-8-31
convoluationalnnneuralnetworkcnnmultiomicsmultiomicsdeepmaptreecitedby:scholar:count:5citedby:scholar:timestamp:2020-8-31
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2Deep learning for inferring gene relationships from single-cell expression data
Y. Yuan, and Z. Bar-Joseph. Proceedings of the National Academy of Sciences, 116 (52): 27151--27158 (2019)
4 years ago by @becker
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cell
cnn
deep
gene
learning
network
neural
nn
prediction
single
cellcnndeepgenelearningnetworkneuralnnpredictionsingle
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3Gaussian Process Prior Variational Autoencoders
F. Casale, A. Dalca, L. Saglietti, J. Listgarten, and N. Fusi. Advances in Neural Information Processing Systems, page 10369--10380. (2018)
5 years ago by @becker
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auto
autoencoder
comparison
encoder
gaussian
generative
model
network
neural
nips
nn
prior
process
time
vae
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autoautoencodercomparisonencodergaussiangenerativemodelnetworkneuralnipsnnpriorprocesstimevaevariation
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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, and Z. Wang. Engineering, 5 (1): 156 - 163 (2019)
5 years ago by @becker
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adversarial
afcs
augmentation
data
gan
generative
model
network
neural
nn
adversarialafcsaugmentationdatagangenerativemodelnetworkneuralnn
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2Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan. Advances in neural information processing systems, page 3483--3491. (2015)
5 years ago by @becker
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afcs
autoencoder
cvae
deep
generative
model
network
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nn
var
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afcsautoencodercvaedeepgenerativemodelnetworkneuralnnvarvariational
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3W-Air: Enabling Personal Air Pollution Monitoring on Wearables
B. Maag, Z. Zhou, and L. Thiele. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2 (1): 24 (2018)
6 years ago by @becker
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p2map
air
quality
eva
everyaware
airprobe
airprobe2
calibration
nn
neural
network
p2mapairqualityevaeveryawareairprobeairprobe2calibrationnnneuralnetwork
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3A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
W. Cheng, Y. Shen, Y. Zhu, and L. Huang. AAAI, (2018)
6 years ago by @becker
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air
attention
eva
everyaware
model
network
neural
nn
p2map
quality
airattentionevaeveryawaremodelnetworkneuralnnp2mapquality
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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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deepscan
neural
networks
nn
survey
review
classification
network
citedby:scholar:count:1502
citedby:scholar:timestamp:2018-5-15
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