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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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1(56) Deep Learning — Andreas Geiger - YouTube
https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD
a year ago by @becker
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1The Best GPUs for Deep Learning in 2020 — An In-depth Analysis
https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/
2 years ago by @becker
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1Best GPU for Deep Learning in 2022 (so far)
https://lambdalabs.com/blog/best-gpu-2022-sofar/
2 years ago by @becker
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1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.
https://github.com/hurcy/awesome-ehr-deeplearning
4 years ago by @becker
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1How to use Data Scaling Improve Deep Learning Model Stability and Performance
https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
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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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
4 years ago by @becker
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1Gaussian Mixture VAE: Lessons in Variational Inference, Generative Models, and Deep Nets - Rui Shu
http://ruishu.io/2016/12/25/gmvae/
5 years ago by @becker
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1[ Paper Summary ] Unsupervised Deep Embedding for Clustering Analysis
https://towardsdatascience.com/paper-summary-unsupervised-deep-embedding-for-clustering-analysis-e269aed77c6b
5 years ago by @becker
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1Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity
N. Ravindra, C. Espinosa, E. Berson, T. Phongpreecha, P. Zhao, M. Becker, A. Chang, S. Shome, I. Marić, D. De Francesco and 1 other author(s). npj Digital Medicine, 6 (1): 171 (2023)
8 months ago by @becker
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accelerometeractivitydeeplearningmyownpregnancyprematurityrepresentationsleep
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4Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, and A. Ho. arXiv preprint arXiv:2211.04325, (2022)
a year ago by @becker
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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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backgroundbioinformaticsdeepinformationknowledgelearningmicribiomenetworkneutralnnthemis
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1Longitudinal variational autoencoder
S. Ramchandran, G. Tikhonov, K. Kujanpää, M. Koskinen, and H. Lähdesmäki. International Conference on Artificial Intelligence and Statistics, page 3898--3906. PMLR, (2021)
2 years ago by @becker
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imputation
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net
nn
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learning
autoencoder
variational
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timeseries
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series
citedby:scholar:count:6
citedby:scholar:timestamp:2022-5-13
imputationgainmissingdatagenerativemodelnetnndeeplearningautoencodervariationallongitudinaltemporaltimeseriestimeseriescitedby:scholar:count:6citedby:scholar:timestamp:2022-5-13
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3Gain: Missing data imputation using generative adversarial nets
J. Yoon, J. Jordon, and M. Schaar. International conference on machine learning, page 5689--5698. PMLR, (2018)
2 years ago by @becker
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2Embedded-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)
2 years ago by @becker
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modeling
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citedby:scholar:timestamp:2022-5-12
toread
citedby:scholar:count:1
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modelingdeepneuralprobabilisticlearningnetworkcitedby:scholar:timestamp:2022-5-12toreadcitedby:scholar:count:1normalizingprogramslayersflowprogramming
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2Leveraging deep contrastive learning for semantic interaction
M. Belcaid, A. Martinez, and J. Leigh. PeerJ Computer Science, (2022)
2 years ago by @becker
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m:
m:id:immermind:node
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m:id:immermind:coordinate:y:-398.4951362609863
umapsemanticdeepcontrastivelearningembeddingimmermindm:m:id:immermind:nodem:id:immermind:coordinate:x:119.61373805999756m:id:immermind:coordinate:y:-398.4951362609863
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1Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE
T. Phongpreecha, C. Gajera, C. Liu, K. Vijayaragavan, A. Chang, M. Becker, R. Fallahzadeh, R. Fernandez, N. Postupna, E. Sherfield and 1 other author(s). Science advances, 7 (51): eabk0473 (2021)
2 years ago by @becker
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alzheimer
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cell
citedby:scholar:count:2
citedby:scholar:timestamp:2022-3-22
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alzheimerbraincellcitedby:scholar:count:2citedby:scholar:timestamp:2022-3-22clusteringdeeplearningmyownnetworkneuralsinglesynapse
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1Bayesian Structure Learning with Generative Flow Networks
T. Deleu, A. Góis, C. Emezue, M. Rankawat, S. Lacoste-Julien, S. Bauer, and Y. Bengio. arXiv preprint arXiv:2202.13903, (2022)
2 years ago by @becker
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toread
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learning
toreadflowmasscytometrycytofbayesneuralnetworksbayesiandeeplearning
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4Causal effect inference with deep latent-variable models
C. Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, and M. Welling. Advances in neural information processing systems, (2017)
2 years ago by @becker
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variational
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enocoder
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model
citedby:scholar:count:360
citedby:scholar:timestamp:2022-2-25
variationalautoencoderautoenocoderlatentvariablecausaleffectinferencedeeplearninggenerativemodelcitedby:scholar:count:360citedby:scholar:timestamp:2022-2-25
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2CytoSet: Predicting clinical outcomes via set-modeling of cytometry data
H. Yi, and N. Stanley. Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, page 1--8. (2021)
2 years ago by @becker
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set
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2The spotlight: A general method for discovering systematic errors in deep learning models
G. d'Eon, J. d'Eon, J. Wright, and K. Leyton-Brown. arXiv preprint arXiv:2107.00758, (2021)
2 years ago by @becker
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citedby:scholar:timestamp:2022-1-21
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2Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review
Y. Si, J. Du, Z. Li, X. Jiang, T. Miller, F. Wang, W. Zheng, and K. Roberts. Journal of Biomedical Informatics, (2021)
2 years ago by @becker
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2A survey on image data augmentation for deep learning
C. Shorten, and T. Khoshgoftaar. Journal of Big Data, 6 (1): 1--48 (2019)
3 years ago by @becker
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citedby:scholar:count:2261
citedby:scholar:timestamp:2021-11-23
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7Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine. International Conference on Machine Learning, page 1126--1135. PMLR, (2017)
3 years ago by @becker
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citedby:scholar:count:4566
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3Temporal phenotyping using deep predictive clustering of disease progression
C. Lee, and M. Van Der Schaar. International Conference on Machine Learning, page 5767--5777. PMLR, (2020)
3 years ago by @becker
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citedby:scholar:timestamp:2021-9-15
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1Meta-Learning in Neural Networks: A Survey
T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2021)
3 years ago by @becker
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4Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila. arXiv preprint arXiv:2006.06676, (2020)
3 years ago by @becker
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citedby:scholar:count:128
citedby:scholar:timestamp:2021-7-20
traininggenerativeadversarialnetworksganlimiteddatasmalldatasetssetmodelneuralnetworkdeeplearningcitedby:scholar:count:128citedby:scholar:timestamp:2021-7-20
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1Machine learning for perturbational single-cell omics
Y. Ji, M. Lotfollahi, F. Wolf, and F. Theis. Cell Systems, 12 (6): 522--537 (2021)
3 years ago by @becker
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wolf
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singlecellperturbationsurveydeeplearningreviewtheiswolfmachinebackgroundknowledgeintegrationgenerativemodelingmodelsmodelomicsmultimultiomics
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3Deep multimodality model for multi-task multi-view learning
L. Zheng, Y. Cheng, and J. He. Proceedings of the 2019 SIAM International Conference on Data Mining, page 10--18. SIAM, (2019)
3 years ago by @becker
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citedby:scholar:count:6
citedby:scholar:timestamp:2021-5-29
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1Chen-Cai-OSU/awesome-equivariant-network: Paper list for equivariant neural network

1(56) Deep Learning — Andreas Geiger - YouTube

1The Best GPUs for Deep Learning in 2020 — An In-depth Analysis

1Best GPU for Deep Learning in 2022 (so far)

1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.

1How to use Data Scaling Improve Deep Learning Model Stability and Performance

1A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

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

1Gaussian Mixture VAE: Lessons in Variational Inference, Generative Models, and Deep Nets - Rui Shu

1[ Paper Summary ] Unsupervised Deep Embedding for Clustering Analysis

publications (hide)38
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1Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

4Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

2Evolution-Informed Neural Networks for Microbiome Data Analysis

1Longitudinal variational autoencoder

3Gain: Missing data imputation using generative adversarial nets

2Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling

2Leveraging deep contrastive learning for semantic interaction

1Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE

1Bayesian Structure Learning with Generative Flow Networks

4Causal effect inference with deep latent-variable models

2CytoSet: Predicting clinical outcomes via set-modeling of cytometry data

2The spotlight: A general method for discovering systematic errors in deep learning models

2Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review

2A survey on image data augmentation for deep learning

7Model-agnostic meta-learning for fast adaptation of deep networks

3Temporal phenotyping using deep predictive clustering of disease progression

1Meta-Learning in Neural Networks: A Survey

4Training generative adversarial networks with limited data

1Machine learning for perturbational single-cell omics

3Deep multimodality model for multi-task multi-view learning

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