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1The Predictive Privacy Project - Rainer Mühlhoff
Data Protection in the Context of Big Data and AI
2 years ago by @hotho
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deep
privacy
data
machine
Datenschutz
predictive
learning
big
ml
deepprivacydatamachineDatenschutzpredictivelearningbigml
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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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twitter
roc
technical
thread
tweet
ml
statistics
auc
twitterroctechnicalthreadtweetmlstatisticsauc
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1Predicting algorithms - machine learning for index structures
https://www.quantamagazine.org/machine-learning-reimagines-the-building-blocks-of-computing-20220315/
3 years ago by @becker
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subgroup
machine
discovery
learning
index
ml
structure
subgroupmachinediscoverylearningindexmlstructure
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1Deterministic python
https://twitter.com/kastnerkyle/status/1473361479143460872?t=-q6YbOcOZ2AXJ_EgW0vkJg&s=19
3 years ago by @becker
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cuda
random
python
seed
machine
learning
numpy
deterministic
torch
ml
cudarandompythonseedmachinelearningnumpydeterministictorchml
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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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visualize
library
learn
pipeline
extension
insights
mlinsights
support
scikit
ml
sklearn
visualizelibrarylearnpipelineextensioninsightsmlinsightssupportscikitmlsklearn
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1Must-read Papers on pre-trained language models
https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/
4 years ago by @hotho
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deep
language
pre-trained
elmo
learning
bert
model
ml
deeplanguagepre-trainedelmolearningbertmodelml
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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
4 years ago by @becker
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transfer
dataset
neural
size
use
machine
learning
to
network
when
ml
nn
transferdatasetneuralsizeusemachinelearningtonetworkwhenmlnn
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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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overview
nalab
code
learn
multiview
python
py
learning
multi
list
view
omics
butterfly
multiomics
ml
overviewnalabcodelearnmultiviewpythonpylearningmultilistviewomicsbutterflymultiomicsml
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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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nalab
learn
multiview
view
python
omics
butterfly
py
multiomics
learning
multi
ml
nalablearnmultiviewviewpythonomicsbutterflypymultiomicslearningmultiml
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1CS 11-747: Neural Networks for NLP
http://phontron.com/class/nn4nlp2019/
4 years ago by @hotho
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nlp
deep
language
learning
ml
nlpdeeplanguagelearningml
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
4 years ago by @becker
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artifical
machine
ai
learning
interpretable
explainable
xai
ml
intelligence
artificalmachineailearninginterpretableexplainablexaimlintelligence
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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
5 years ago by @becker
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acc
comparison
roc
precision
data
accuracy
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metric
auc
science
mining
recall
evaluation
machine
metrics
ml
acccomparisonrocprecisiondataaccuracylearningmetricaucscienceminingrecallevaluationmachinemetricsml
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1KI made in Germany
https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf
5 years ago by @hotho
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paper
deep
white
machine
ai
learning
ki
ml
Germany
paperdeepwhitemachineailearningkimlGermany
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1Kara Primer
A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team
5 years ago by @hotho
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analytics
primer
kara
privacy
data
Datenschutz
Medizin
ml
analyticsprimerkaraprivacydataDatenschutzMedizinml
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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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machine
mlflowhelper
learning
tracking
mlflow
ml
machinemlflowhelperlearningtrackingmlflowml
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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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science
systems
data
machine
libraries
learning
ml
list
sciencesystemsdatamachinelibrarieslearningmllist
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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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projects
monitoring
data
learning
tracking
mlflow
science
productivity
machine
logger
logging
managing
sacred
model
ml
projectsmonitoringdatalearningtrackingmlflowscienceproductivitymachineloggerloggingmanagingsacredmodelml
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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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science
lifecycle
python
productivity
data
machine
logger
logging
learning
mlflow
ml
sciencelifecyclepythonproductivitydatamachineloggerlogginglearningmlflowml
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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
python
visualization
machine
logger
logging
learning
sacred
mongodb
ml
omniboardpythonvisualizationmachineloggerlogginglearningsacredmongodbml
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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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python
machine
logger
logging
client
learning
sacred
mongodb
incense
ml
pythonmachineloggerloggingclientlearningsacredmongodbincenseml
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publications (hide)48
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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)
8 months ago by @becker
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ien
linear
background
machine
regression
learning
knowledge
bki
ml
informed
integration
ienlinearbackgroundmachineregressionlearningknowledgebkimlinformedintegration
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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)
8 months ago by @becker
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neural
background
gnn
machine
learning
knowledge
networks
graph
bki
ml
informed
integration
neuralbackgroundgnnmachinelearningknowledgenetworksgraphbkimlinformedintegration
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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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artificial
responsible
machine
ai
citedby:scholar:count:15
learning
intelligence
ml
citedby:scholar:timestamp:2023-4-10
accountability
artificialresponsiblemachineaicitedby:scholar:count:15learningintelligencemlcitedby:scholar:timestamp:2023-4-10accountability
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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)
2 years ago by @becker
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comparison
forest
decision
neural
data
tabular
learning
tree
trees
network
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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1Interpretable machine learning prediction of all-cause mortality
W. Qiu, H. Chen, A. Dincer, S. Lundberg, M. Kaeberlein, and S. Lee. medRxiv, (2022)
2 years ago by @becker
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supervised
distance
citedby:scholar:count:3
machine
collaboration
fuelln
learning
interpretable
citedby:scholar:timestamp:2022-11-30
ml
superviseddistancecitedby:scholar:count:3machinecollaborationfuellnlearninginterpretablecitedby:scholar:timestamp:2022-11-30ml
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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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myown
machine
actigraphy
learning
medicine
recovery
iti
ml
surgery
myownmachineactigraphylearningmedicinerecoveryitimlsurgery
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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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citedby:scholar:count:51
art
artifical
ai
learning
medicine
health
intelligence
multi
ehr
the
directions
omics
of
citedby:scholar:timestamp:2021-7-10
machine
future
multiomics
challenges
state
sota
ml
care
citedby:scholar:count:51artartificalailearningmedicinehealthintelligencemultiehrthedirectionsomicsofcitedby:scholar:timestamp:2021-7-10machinefuturemultiomicschallengesstatesotamlcare
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1Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. Coley, C. Xiao, J. Sun, and M. Zitnik. (2021)cite arxiv:2102.09548.
4 years ago by @hotho
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medical
datasets
Medizin
ml
medicaldatasetsMedizinml
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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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high
immune
system
dimensional
machine
gating
learning
immunology
ml
single
cell
manual
highimmunesystemdimensionalmachinegatinglearningimmunologymlsinglecellmanual
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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)
5 years ago by @becker
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variational
standard
autoencoder
auto
outperform
deep
outerforms
neural
dl
learning
encoder
representation
network
machine
vae
representational
ml
variationalstandardautoencoderautooutperformdeepouterformsneuraldllearningencoderrepresentationnetworkmachinevaerepresentationalml
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6Comparison of System Call Representations for Intrusion Detection.
S. Wunderlich, M. Ring, D. Landes, and A. Hotho. CISIS-ICEUTE, volume 951 of Advances in Intelligent Systems and Computing, page 14-24. Springer, (2019)
5 years ago by @hotho
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imported
myown
2019
system
call
ids
ml
importedmyown2019systemcallidsml
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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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random
fail
problem
forest
machine
learning
forests
ml
randomfailproblemforestmachinelearningforestsml
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2Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
M. Raissi, P. Perdikaris, and G. Karniadakis. (2017)cite arxiv:1711.10566.
6 years ago by @hotho
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deep
physics
learning
ml
deepphysicslearningml
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2Harnessing deep neural networks with logic rules
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing. arXiv preprint arXiv:1603.06318, (2016)
6 years ago by @becker
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citedby:scholar:timestamp:2019-1-28
background
bi
machine
learning
information
ml
citedby:scholar:count:124
citedby:scholar:timestamp:2019-1-28backgroundbimachinelearninginformationmlcitedby:scholar:count:124
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1Observations on bagging
A. Buja, and W. Stuetzle. Statistica Sinica, (2006)
6 years ago by @becker
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citedby:scholar:count:85
citedby:scholar:timestamp:2019-1-17
bagging
machine
learning
ensemble
bootstrapping
ml
citedby:scholar:count:85citedby:scholar:timestamp:2019-1-17baggingmachinelearningensemblebootstrappingml
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3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
C. Brokamp, R. Jandarov, M. Rao, G. LeMasters, and P. Ryan. Atmospheric Environment, (2017)
7 years ago by @becker
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comparison
linear
forest
citedby:scholar:count:11
use
air
lur
learning
quality
eva
pollution
random
machine
regression
land
citedby:scholar:timestamp:2018-2-21
p2map
ml
comparisonlinearforestcitedby:scholar:count:11useairlurlearningqualityevapollutionrandommachineregressionlandcitedby:scholar:timestamp:2018-2-21p2mapml
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7A few useful things to know about machine learning
P. Domingos. Communications of the ACM, 55 (10): 78--87 (2012)
7 years ago by @hotho
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toread
machine
learning
ml
toreadmachinelearningml
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2b+ WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring—Preliminary results and analysis
F. Murphy, M. Magno, P. Whelan, and E. Vici. Sensors Applications Symposium (SAS), 2015 IEEE, page 1--6. IEEE, (2015)
7 years ago by @becker
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bee
hobos
citedby:scholar:count:11
honey
machine
prediction
citedby:scholar:timestamp:2017-7-20
learning
bees
ml
weather
beehoboscitedby:scholar:count:11honeymachinepredictioncitedby:scholar:timestamp:2017-7-20learningbeesmlweather
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1b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring
F. Edwards-Murphy, M. Magno, P. Whelan, J. O’Halloran, and E. Popovici. Computers and Electronics in Agriculture, (2016)
7 years ago by @becker
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bee
hobos
citedby:scholar:count:4
honey
machine
prediction
citedby:scholar:timestamp:2017-7-20
learning
bees
ml
weather
beehoboscitedby:scholar:count:4honeymachinepredictioncitedby:scholar:timestamp:2017-7-20learningbeesmlweather
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7Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
F. Morstatter, J. Pfeffer, H. Liu, and K. Carley. (2013)
12 years ago by @hotho
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toread
dm
comparing
twitters
sample
ml
toreaddmcomparingtwitterssampleml
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