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1Technical threads
https://twitter.com/docmilanfar/status/1528179986393247744?t=UvZ0PICIMAMFEGG2KlYDhQ&s=19
2 years ago by @becker
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1Predicting algorithms - machine learning for index structures
https://www.quantamagazine.org/machine-learning-reimagines-the-building-blocks-of-computing-20220315/
2 years ago by @becker
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discovery
index
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machine
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discoveryindexlearningmachinemlstructuresubgroup
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1Deterministic python
https://twitter.com/kastnerkyle/status/1473361479143460872?t=-q6YbOcOZ2AXJ_EgW0vkJg&s=19
2 years ago by @becker
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cuda
deterministic
learning
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ml
numpy
python
random
seed
torch
cudadeterministiclearningmachinemlnumpypythonrandomseedtorch
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1Notebooks Gallery — mlinsights
http://www.xavierdupre.fr/app/mlinsights/helpsphinx/all_notebooks.html
3 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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transferlearningwhentousemlneuralnetworknnmachinedatasetsize
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1MULTI-VIEW LEARNING | Papers With Code
https://paperswithcode.com/task/multi-view-learning/
4 years ago by @becker
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1Overview of mvlearn — mvlearn alpha documentation
https://mvlearn.neurodata.io/index.html
4 years ago by @becker
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
4 years ago by @becker
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1machine learning - Getting a low ROC AUC score but a high accuracy - Stack Overflow
https://stackoverflow.com/questions/47104129/getting-a-low-roc-auc-score-but-a-high-accuracy/47111246#47111246
4 years ago by @becker
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1mgbckr/mlflowhelper: Various tools for working with mlflow (see https://mlflow.org)
https://github.com/mgbckr/mlflowhelper
5 years ago by @becker
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1EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
https://github.com/EthicalML/awesome-production-machine-learning
5 years ago by @becker
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1[D] How do you manage your machine learning experiments? : MachineLearning
https://www.reddit.com/r/MachineLearning/comments/bx0apm/d_how_do_you_manage_your_machine_learning/
5 years ago by @becker
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1MLflow - A platform for the machine learning lifecycle | MLflow
https://mlflow.org/
5 years ago by @becker
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python
mlflow
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pythonmlflowproductivityloggingloggermlmachinelearningdatasciencelifecycle
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1vivekratnavel/omniboard: Web-based dashboard for Sacred
https://github.com/vivekratnavel/omniboard
5 years ago by @becker
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omniboard
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sacredpythonomniboardvisualizationmongodbmlmachinelearningloggerlogging
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1JarnoRFB/incense: Interactively retrieve data from sacred experiments.
https://github.com/JarnoRFB/incense
5 years ago by @becker
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1incense/demo.ipynb at master · JarnoRFB/incense
https://github.com/JarnoRFB/incense/blob/master/demo.ipynb
5 years ago by @becker
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python
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1Algorithms | Feature Selection @ ASU
http://featureselection.asu.edu/algorithms.php
5 years ago by @becker
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selection
python
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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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explainable
machine
learning
ml
explainability
interpretability
interpretation
neural
network
nn
code
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python
explainablemachinelearningmlexplainabilityinterpretabilityinterpretationneuralnetworknncodeimplementationshapshaplypython
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1lasso - default lambda sequence in glmnet for cross-validation - Stack Overflow
https://stackoverflow.com/questions/23686067/default-lambda-sequence-in-glmnet-for-cross-validation
5 years ago by @becker
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1Welcome to REP’s documentation! — REP (Reproducible Experiment Platform) 0.6.7 documentation
http://yandex.github.io/rep/
5 years ago by @becker
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sklearn
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learn
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REP
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1Feature-weighted elastic net: using "features of features" for better prediction
J. Tay, N. Aghaeepour, T. Hastie, and R. Tibshirani. Statistica Sinica, 33 (1): 259--279 (January 2023)
2 months ago by @becker
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background
bki
ien
informed
integration
knowledge
learning
linear
machine
ml
regression
backgroundbkiieninformedintegrationknowledgelearninglinearmachinemlregression
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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 months ago by @becker
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backgroundbkignngraphinformedintegrationknowledgelearningmachinemlnetworksneural
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2Machine learning techniques for accountability
B. Kim, and F. Doshi-Velez. AI Magazine, 42 (1): 47--52 (2021)
a year ago by @becker
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ai
artificial
intelligence
machine
learning
ml
accountability
responsible
citedby:scholar:count:15
citedby:scholar:timestamp:2023-4-10
aiartificialintelligencemachinelearningmlaccountabilityresponsiblecitedby:scholar:count:15citedby:scholar:timestamp:2023-4-10
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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
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decision
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forest
learning
machine
ml
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nn
performance
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citedby:scholar:count:5citedby:scholar:timestamp:2022-12-1comparisondatadecisionevaluationforestlearningmachinemlnetworkneuralnnperformancerandomtabulartreetrees
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1Interpretable machine learning prediction of all-cause mortality
W. Qiu, H. Chen, A. Dincer, S. Lundberg, M. Kaeberlein, and S. Lee. medRxiv, (2022)
a year ago by @becker
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machine
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collaboration
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citedby:scholar:timestamp:2022-11-30
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1Objective Activity Parameters Track Patient-Specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States
R. Fallahzadeh, F. Verdonk, E. Ganio, A. Culos, N. Stanley, I. Maric, A. Chang, M. Becker, T. Phongpreecha, M. Xenochristou and 1 other author(s). Annals of Surgery, (October 2021)
3 years ago by @becker
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iti
learning
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myown
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actigraphyitilearningmachinemedicinemlmyownrecoverysurgery
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1AI in health: state of the art, challenges, and future directions
F. Wang, and A. Preininger. Yearbook of medical informatics, 28 (01): 016--026 (2019)
3 years ago by @becker
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ai
ml
machine
learning
artifical
intelligence
challenges
state
of
the
art
sota
future
directions
health
care
medicine
multi
omics
multiomics
ehr
citedby:scholar:count:51
citedby:scholar:timestamp:2021-7-10
aimlmachinelearningartificalintelligencechallengesstateoftheartsotafuturedirectionshealthcaremedicinemultiomicsmultiomicsehrcitedby:scholar:count:51citedby:scholar:timestamp:2021-7-10
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1Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology
S. Palit, C. Heuser, G. de Almeida, F. Theis, and C. Zielinski. Frontiers in Immunology, (July 2019)
4 years ago by @becker
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cell
dimensional
gating
high
immune
immunology
learning
machine
manual
ml
single
system
celldimensionalgatinghighimmuneimmunologylearningmachinemanualmlsinglesystem
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1Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
A. Smith, J. Walsh, J. Long, C. Davis, P. Henstock, M. Hodge, M. Maciejewski, X. Mu, S. Ra, S. Zhao and 1 other author(s). BMC Bioinformatics, 21 (1): 1--18 (2020)
4 years ago by @becker
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auto
autoencoder
deep
dl
encoder
learning
machine
ml
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outerforms
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standard
vae
variational
autoautoencoderdeepdlencoderlearningmachinemlnetworkneuralouterformsoutperformrepresentationrepresentationalstandardvaevariational
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2When do random forests fail?
C. Tang, D. Garreau, and U. von Luxburg. Advances in Neural Information Processing Systems, page 2983--2993. (2018)
5 years ago by @becker
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forest
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failforestforestslearningmachinemlproblemrandom
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BibSonomy

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1Technical threads

1Predicting algorithms - machine learning for index structures

1Deterministic python

1Notebooks Gallery — mlinsights

3Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science

1MULTI-VIEW LEARNING | Papers With Code

1Overview of mvlearn — mvlearn alpha documentation

4Interpretable Machine Learning

1machine learning - Getting a low ROC AUC score but a high accuracy - Stack Overflow

1mgbckr/mlflowhelper: Various tools for working with mlflow (see https://mlflow.org)

1EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

1[D] How do you manage your machine learning experiments? : MachineLearning

1MLflow - A platform for the machine learning lifecycle | MLflow

1vivekratnavel/omniboard: Web-based dashboard for Sacred

1JarnoRFB/incense: Interactively retrieve data from sacred experiments.

1incense/demo.ipynb at master · JarnoRFB/incense

1Algorithms | Feature Selection @ ASU

2slundberg/shap: A unified approach to explain the output of any machine learning model.

1lasso - default lambda sequence in glmnet for cross-validation - Stack Overflow

1Welcome to REP’s documentation! — REP (Reproducible Experiment Platform) 0.6.7 documentation

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1Feature-weighted elastic net: using "features of features" for better prediction

2Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer

2Machine learning techniques for accountability

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

1Interpretable machine learning prediction of all-cause mortality

1Objective Activity Parameters Track Patient-Specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States

1AI in health: state of the art, challenges, and future directions

1Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology

1Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

2When do random forests fail?

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