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1NeurIPS 2022 Gaze Meets ML Workshop
https://groups.google.com/g/ml-news/c/F0JgTZQL-Ns/m/H95PMQyTAgAJ?pli=1
10 months ago by @becker
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1Healthcare Algorithms Don’t Always Need to Be Generalizable
A Stanford researcher questions the need for generalizable models and proposes instead sharing recipes for creating useful local models.
11 months ago by @becker
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1GitHub - grok-ai/nn-template: Generic template to bootstrap your PyTorch project.
https://github.com/grok-ai/nn-template
a year ago by @becker
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1How to be a machine learning biologist
https://twitter.com/natstann/status/1516428383432941571?t=RGNmFPvykzWjdvYCkoN2oA&s=19
a year 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/
a year ago by @becker
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1microsoft/responsible-ai-toolbox: This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
https://github.com/microsoft/responsible-ai-toolbox
a year ago by @becker
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1Deterministic python
https://twitter.com/kastnerkyle/status/1473361479143460872?t=-q6YbOcOZ2AXJ_EgW0vkJg&s=19
a year ago by @becker
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1‪Sara Hajian‬ - ‪Google Scholar‬
https://scholar.google.de/citations?hl=en&user=rXY4178AAAAJ&view_op=list_works&sortby=pubdate
2 years ago by @becker
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
2 years ago by @becker
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2CORNELL CS4780 "Machine Learning for Intelligent Systems" - YouTube
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
2 years ago by @becker
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119: Boosting
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
2 years ago by @becker
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4How to pick more beautiful colors for your data visualizations | Chartable
https://blog.datawrapper.de/beautifulcolors/
3 years ago by @becker
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choose
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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
3 years ago by @becker
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transfer
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ml
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machine
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transferlearningwhentousemlneuralnetworknnmachinedatasetsize
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1GFLASSO: Graph-Guided Fused LASSO in R - DataCamp
https://www.datacamp.com/community/tutorials/gflasso-R
3 years ago by @becker
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r
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graphguidedlassomachinelearningrmultilabelmultilabel
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1mims-harvard/scikit-fusion: scikit-fusion: Data fusion via collective latent factor models
https://github.com/mims-harvard/scikit-fusion
3 years ago by @becker
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fusion
mit
multiview
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2Statistical Significance Tests for Comparing Machine Learning Algorithms
https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
3 years ago by @becker
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statistical
significance
test
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validation
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3Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
3 years ago by @becker
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interpretable
machine
learning
ml
xai
ai
artifical
intelligence
explainable
interpretablemachinelearningmlxaiaiartificalintelligenceexplainable
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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/
3 years ago by @becker
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deep
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scaling
input
output
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butterfly
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1Don’t Sweat the Solver Stuff - Towards Data Science
https://towardsdatascience.com/dont-sweat-the-solver-stuff-aea7cddc3451
3 years ago by @becker
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logistic
regression
sklearn
linear
model
machine
learning
data
science
solver
python
py
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1arvkevi/kneed: Knee point detection in Python
https://github.com/arvkevi/kneed
3 years ago by @becker
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elbow
plot
method
clustering
number
of
clusters
cluster
python
data
science
mining
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kneedle
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1Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L. Oakden-Rayner, J. Dunnmon, G. Carneiro, and C. Ré. Proceedings of the ACM conference on health, inference, and learning , page 151--159. (2020)
4 days ago by @becker
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hidden
stratification
subgroup
machine
learning
failure
pattern
mining
citedby:scholar:count:243
citedby:scholar:timestamp:2023-5-31
hiddenstratificationsubgroupmachinelearningfailurepatternminingcitedby:scholar:count:243citedby:scholar:timestamp:2023-5-31
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1Machine learning techniques for accountability
B. Kim, and F. Doshi-Velez. AI Magazine 42 (1): 47--52 (2021)
2 months 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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1Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning
M. Becker, J. Dai, A. Chang, D. Feyaerts, I. Stelzer, M. Zhang, E. Berson, G. Saarunya, D. De Francesco, C. Espinosa, and others. Frontiers in Pediatrics (2022)
5 months ago by @becker
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adverse
cell
immune
immunology
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life
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single
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system
adversecellimmuneimmunologylearninglifemachinemultitaskmyownoutcomespregnancysinglestressstressorssystem
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1Machine Learning Techniques
J. Choi, N. Aghaeepour, and M. Becker. page 147--164. Springer International Publishing, Cham, (2022)
5 months ago by @becker
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book
chapter
learning
machine
myown
surgery
bookchapterlearningmachinemyownsurgery
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3Will 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)
5 months ago by @becker
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themis
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requirement
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themislargedatamachinelearningrequirementdeepmodelsmodeldatasetdatasetssetsetslimit
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1The Deskilling of Domain Expertise in AI Development
N. Sambasivan, and R. Veeraraghavan. CHI Conference on Human Factors in Computing Systems , page 1--14. (2022)
6 months ago by @becker
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knowledge
themis
domain
expertise
ai
artificial
intelligence
machine
learning
deskilling
citedby:scholar:count:13
citedby:scholar:timestamp:2022-12-8
knowledgethemisdomainexpertiseaiartificialintelligencemachinelearningdeskillingcitedby:scholar:count:13citedby:scholar:timestamp:2022-12-8
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1Why 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)
6 months 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
neural
nn
performance
random
tabular
tree
trees
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)
6 months ago by @becker
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fuelln
interpretable
machine
learning
ml
supervised
distance
collaboration
citedby:scholar:count:3
citedby:scholar:timestamp:2022-11-30
fuellninterpretablemachinelearningmlsuperviseddistancecollaborationcitedby:scholar:count:3citedby:scholar:timestamp:2022-11-30
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1Algorithmic fairness datasets: the story so far
A. Fabris, S. Messina, G. Silvello, and G. Susto. Data Mining and Knowledge Discovery 36 (6): 2074--2152 (Nov 1, 2022)
6 months ago by @becker
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algorithmic
fairness
learning
machine
themis
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1A guide to machine learning for biologists
J. Greener, S. Kandathil, L. Moffat, and D. Jones. Nature Reviews Molecular Cell Biology 23 (1): 40--55 (2022)
11 months ago by @becker
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guide
machine
learning
biologists
nature
citedby:scholar:count:72
citedby:scholar:timestamp:2022-6-29
guidemachinelearningbiologistsnaturecitedby:scholar:count:72citedby:scholar:timestamp:2022-6-29
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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)
11 months ago by @becker
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explainable
machine
learning
neural
networks
themis
citedby:scholar:count:193
citedby:scholar:timestamp:2022-6-27
explainablemachinelearningneuralnetworksthemiscitedby:scholar:count:193citedby:scholar:timestamp:2022-6-27
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1A biomedical open knowledge network harnesses the power of AI to understand deep human biology
S. Baranzini, K. Börner, J. Morris, C. Nelson, K. Soman, E. Schleimer, M. Keiser, M. Musen, R. Pearce, T. Reza, and others. Ai Magazine 43 (1): 46--58 (2022)
12 months ago by @becker
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ai
background
biology
biomedical
citedby:scholar:count:1
citedby:scholar:timestamp:2022-6-21
graph
human
knowledge
learning
machine
network
open
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2Integrating Machine Learning with Human Knowledge
C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu. iScience 23 (11): 101656 (2020)
a year ago by @becker
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integration
knowledge
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1Leukocyte telomere dynamics across gestation in uncomplicated pregnancies and associations with stress
D. Panelli, S. Leonard, R. Wong, M. Becker, J. Mayo, E. Wu, A. Girsen, I. Gotlib, N. Aghaeepour, M. Druzin, and others. BMC Pregnancy and Childbirth 22 (1): 1--12 (2022)
a year ago by @becker
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learning
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myown
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learningmachinemyownpregnancystresstelomeretelomeres
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5Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet. Information 11 (4): 193 (2020)
a year ago by @becker
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ai
artifical
data
gpu
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library
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paper:fastcor
py
python
rapids
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aiartificaldatagpuintelligencelearninglibrarymachinepaper:fastcorpypythonrapidsscience
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1MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
T. Kyono, Y. Zhang, A. Bellot, and M. van der Schaar. Advances in Neural Information Processing Systems (2021)
a year ago by @becker
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causal
citedby:scholar:count:1
citedby:scholar:timestamp:2022-3-21
data
imputation
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learning
machine
missing
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1Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System
L. Shenhav, and M. Azad. mSystems 7 (1): e01132--21 (2022)
a year ago by @becker
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milk
computational
machine
learning
human
review
survey
milkcomputationalmachinelearninghumanreviewsurvey
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1AI in health and medicine
P. Rajpurkar, E. Chen, O. Banerjee, and E. Topol. Nature Medicine (2022)
a year ago by @becker
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ai
artificial
citedby:scholar:count:1
citedby:scholar:timestamp:2022-2-9
health
intelligence
learning
machine
medicine
nature
review
survey
aiartificialcitedby:scholar:count:1citedby:scholar:timestamp:2022-2-9healthintelligencelearningmachinemedicinenaturereviewsurvey
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2Explaining by removing: A unified framework for model explanation
I. Covert, S. Lundberg, and S. Lee. Journal of Machine Learning Research 22 (209): 1--90 (2021)
2 years ago by @becker
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explaining
explainable
ai
artificial
intelligence
machine
learning
interpretable
feature
importance
citedby:scholar:count:13
citedby:scholar:timestamp:2021-11-23
explainingexplainableaiartificialintelligencemachinelearninginterpretablefeatureimportancecitedby:scholar:count:13citedby:scholar:timestamp:2021-11-23
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1Cross-validation is safe to use
R. King, O. Orhobor, and C. Taylor. Nature Machine Intelligence 3 (4): 276--276 (2021)
2 years ago by @becker
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cross
validation
nature
machine
intelligence
evaluation
learning
citedby:scholar:count:2
citedby:scholar:timestamp:2021-11-23
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