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1Gopher and RETRO Prove Lean Language Models Can Push Boundaries
https://www.deeplearning.ai/the-batch/large-language-models-shrink/?utm_campaign=The%20Batch&utm_medium=email&_hsmi=240401361&_hsenc=p2ANqtz-8xSUusSBIkSJlMHiucL2H3K9ukcSqlS-6aE-4TxPD5Bh1ElshDiBx1Vs-ROguVWC1JTS-wlfpgjJtgeuN18dgvmvEbtA&utm_content=240401361&utm_source=hs_email
2 years ago by @becker
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shrink
large
language
data
skrinking
less
model
themis
shrinklargelanguagedataskrinkinglessmodelthemis
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1Titanic Model with 90% accuracy | Kaggle
https://www.kaggle.com/code/vinothan/titanic-model-with-90-accuracy
2 years ago by @becker
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science
kaggle
titanic
data
sciencekaggletitanicdata
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1kaggle-titanic/titanic-solution.ipynb at master · minsuk-heo/kaggle-titanic
https://github.com/minsuk-heo/kaggle-titanic/blob/master/titanic-solution.ipynb
2 years ago by @becker
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science
titanic
data
sciencetitanicdata
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1Data Science Club | Becker Lab
Hallo
2 years ago by @becker
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science
dsc
dts
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club
dtsclub
sciencedscdtsdataclubdtsclub
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1Tired: PCA + kmeans, Wired: UMAP + GMM | R-bloggers
https://www.r-bloggers.com/2021/06/tired-pca-kmeans-wired-umap-gmm/
2 years ago by @becker
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umap
science
comparison
data
gmm
kmeans
pca
combination
umapsciencecomparisondatagmmkmeanspcacombination
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1shafferm/SCNIC: Sparse Cooccurence Network Investigation for Compositional data
https://github.com/shafferm/SCNIC
2 years ago by @becker
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co-occurrence
data
correlation
compositional
positive
analysis
project:fastcor
network
coocurrence
co-occurrencedatacorrelationcompositionalpositiveanalysisproject:fastcornetworkcoocurrence
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1leon-thomm/Ryven: Flow-based visual scripting for Python
https://github.com/leon-thomm/Ryven
2 years ago by @becker
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science
jupyter
python
data
dag
notebook
programming
sciencejupyterpythondatadagnotebookprogramming
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1wonderworks-software/PyFlow: Visual scripting framework for python - https://wonderworks-software.github.io/PyFlow
https://github.com/wonderworks-software/PyFlow
2 years ago by @becker
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science
jupyter
python
data
dag
notebook
programming
sciencejupyterpythondatadagnotebookprogramming
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1DAG Based notebooks? - JupyterLab - Jupyter Community Forum
https://discourse.jupyter.org/t/dag-based-notebooks/11173
2 years ago by @becker
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science
jupyter
python
data
dag
notebook
programming
sciencejupyterpythondatadagnotebookprogramming
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3Galaxy
https://usegalaxy.org/
2 years ago by @becker
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code
gene
genes
data
intensice
biomedical
tools
alignment
research
platform
codegenegenesdataintensicebiomedicaltoolsalignmentresearchplatform
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1zqfang/GSEApy: Gene Set Enrichment Analysis in Python
https://github.com/zqfang/GSEApy
2 years ago by @becker
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enrichment
set
database
gene
python
data
py
api
analysis
base
enrichmentsetdatabasegenepythondatapyapianalysisbase
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1Data Visualization in AR/VR
https://www.compassred.com/data-journal/data-visualization-in-arvr
3 years ago by @becker
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visualization
toolkit
data
vr
immermind
tools
toolkits
visualizationtoolkitdatavrimmermindtoolstoolkits
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1Home - Immersion Analytics
https://www.immersionanalytics.com/
3 years ago by @becker
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analytics
visualization
data
company
vr
immermind
analyticsvisualizationdatacompanyvrimmermind
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1Augmented reality platform for Enterprise | Magic Leap
https://www.magicleap.com/en-us
3 years ago by @becker
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visualization
headset
data
vr
immermind
visualizationheadsetdatavrimmermind
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1Virtualitics: Make Smarter AI-Driven Decisions | AI Data Analysis Software
https://virtualitics.com/
3 years ago by @becker
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visualization
data
company
vr
immermind
visualizationdatacompanyvrimmermind
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1Immersion Analytics: 1st Place Winner - Tableau 2021 DATAdev Hackathon - YouTube
https://www.youtube.com/watch?v=hYxPO-bfbOk
3 years ago by @becker
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tableau
visualization
data
vr
immermind
tableauvisualizationdatavrimmermind
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1Data Storytelling with Immersive Visualizations
https://flowimmersive.com/
3 years ago by @becker
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visualization
data
vr
immermind
visualizationdatavrimmermind
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1OHDSI/Aphrodite: [in development]
https://github.com/OHDSI/Aphrodite
3 years ago by @becker
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package
fairness
r
subgroup
subgroups
race
aphrodite
bias
data
patient
medicine
heterogeneity
packagefairnessrsubgroupsubgroupsraceaphroditebiasdatapatientmedicineheterogeneity
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1Siren - Interactive and visual redescription mining — Siren - Interactive Redescription Mining
http://cs.uef.fi/siren/main/index.html
3 years ago by @becker
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siren
subgroup
clired
python
data
py
interactive
redescription
geo
mining
geospatial
redescriptions
spatial
sirensubgroupcliredpythondatapyinteractiveredescriptiongeomininggeospatialredescriptionsspatial
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
3 years ago by @becker
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medical
data
learning
discovery
representation
biomedical
network
networks
graph
dataset
graphs
subgraph
machine
medicaldatalearningdiscoveryrepresentationbiomedicalnetworknetworksgraphdatasetgraphssubgraphmachine
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1Data-driven longitudinal characterization of neonatal health and morbidity
D. Francesco, J. Reiss, J. Roger, A. Tang, A. Chang, M. Becker, T. Phongpreecha, C. Espinosa, S. Morin, E. Berson and 23 other author(s). Science Translational Medicine, 15 (683): eadc9854 (2023)
a year ago by @becker
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myown
project:2023-tracking
data
health
neonatal
paper:fastcor
longitudinal
myownproject:2023-trackingdatahealthneonatalpaper:fastcorlongitudinal
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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)
2 years ago by @becker
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set
models
deep
large
sets
data
learning
requirement
datasets
dataset
machine
limit
model
themis
setmodelsdeeplargesetsdatalearningrequirementdatasetsdatasetmachinelimitmodelthemis
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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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1Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models
E. Saccenti, M. Hendriks, and A. Smilde. Scientific reports, 10 (1): 1--19 (2020)
2 years ago by @becker
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estimation
citedby:scholar:timestamp:2022-9-23
citedby:scholar:count:53
error
bias
correlation
data
paper:fastcor
correction
estimationcitedby:scholar:timestamp:2022-9-23citedby:scholar:count:53errorbiascorrelationdatapaper:fastcorcorrection
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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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variational
autoencoder
deep
data
citedby:scholar:count:6
learning
gain
citedby:scholar:timestamp:2022-5-13
timeseries
imputation
series
time
missing
model
net
temporal
nn
generative
longitudinal
variationalautoencoderdeepdatacitedby:scholar:count:6learninggaincitedby:scholar:timestamp:2022-5-13timeseriesimputationseriestimemissingmodelnettemporalnngenerativelongitudinal
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2Gain: 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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deep
adversarial
data
learning
gain
citedby:scholar:timestamp:2022-5-13
citedby:scholar:count:468
imputation
missing
model
net
nn
generative
deepadversarialdatalearninggaincitedby:scholar:timestamp:2022-5-13citedby:scholar:count:468imputationmissingmodelnetnngenerative
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4Machine 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)
2 years ago by @becker
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python
rapids
artifical
data
ai
py
learning
gpu
intelligence
paper:fastcor
science
library
machine
pythonrapidsartificaldataaipylearninggpuintelligencepaper:fastcorsciencelibrarymachine
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1RAPIDS: Collection of Libraries for End to End GPU Data Science
R. Team. (2018)
2 years ago by @becker
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science
library
python
rapids
data
py
gpu
paper:fastcor
sciencelibrarypythonrapidsdatapygpupaper:fastcor
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2MIRACLE: 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)
2 years ago by @becker
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toread
citedby:scholar:count:1
data
imputation
machine
impute
learning
citedby:scholar:timestamp:2022-3-21
missing
causal
value
toreadcitedby:scholar:count:1dataimputationmachineimputelearningcitedby:scholar:timestamp:2022-3-21missingcausalvalue
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1A knowledge graph to interpret clinical proteomics data
A. Santos, A. Colaco, A. Nielsen, L. Niu, M. Strauss, P. Geyer, F. Coscia, N. Albrechtsen, F. Mundt, L. Jensen and 1 other author(s). Nature Biotechnology, (2022)
2 years ago by @becker
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domain
clinical
proteomics
background
data
medicine
knowledge
network
graph
domainclinicalproteomicsbackgrounddatamedicineknowledgenetworkgraph
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1Wilds: A benchmark of in-the-wild distribution shifts
P. Koh, S. Sagawa, H. Marklund, S. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. Phillips, I. Gao and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)
3 years ago by @becker
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shift
m:id:undefined:coordinate:x:-251.9265022277832
data
citedby:scholar:timestamp:2022-1-21
datasets
distribution
m:
m:id:undefined:node
citedby:scholar:count:135
benchmark
m:id:undefined:coordinate:y:-169.61904335021973
shiftm:id:undefined:coordinate:x:-251.9265022277832datacitedby:scholar:timestamp:2022-1-21datasetsdistributionm:m:id:undefined:nodecitedby:scholar:count:135benchmarkm:id:undefined:coordinate:y:-169.61904335021973
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3Imaxes: Immersive axes as embodied affordances for interactive multivariate data visualisation
M. Cordeil, A. Cunningham, T. Dwyer, B. Thomas, and K. Marriott. Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, page 71--83. (2017)
3 years ago by @becker
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virtual
citedby:scholar:count:140
reality
data
analysis
immermind
m:
plots
citedby:scholar:timestamp:2022-1-11
m:id:immermind:coordinate:y:134.9595603942871
m:id:immermind:node
vr
m:id:immermind:coordinate:x:-378.8338394165039
virtualcitedby:scholar:count:140realitydataanalysisimmermindm:plotscitedby:scholar:timestamp:2022-1-11m:id:immermind:coordinate:y:134.9595603942871m:id:immermind:nodevrm:id:immermind:coordinate:x:-378.8338394165039
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1A 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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image
citedby:scholar:timestamp:2021-11-23
deep
citedby:scholar:count:2261
data
learning
survey
augmentation
imagecitedby:scholar:timestamp:2021-11-23deepcitedby:scholar:count:2261datalearningsurveyaugmentation
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2BayReL: Bayesian Relational Learning for Multi-omics Data Integration
E. Hajiramezanali, A. Hasanzadeh, N. Duffield, K. Narayanan, and X. Qian. Advances in Neural Information Processing Systems, (2020)
3 years ago by @becker
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bayesian
modeling
omics
bayes
data
multiomics
learning
relational
multi
integration
bayesianmodelingomicsbayesdatamultiomicslearningrelationalmultiintegration
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2Training 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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set
small
deep
neural
limited
adversarial
data
training
learning
datasets
network
networks
gan
citedby:scholar:count:128
citedby:scholar:timestamp:2021-7-20
model
generative
setsmalldeepneurallimitedadversarialdatatraininglearningdatasetsnetworknetworksgancitedby:scholar:count:128citedby:scholar:timestamp:2021-7-20modelgenerative
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1Data-Driven Modeling of Pregnancy-Related Complications
C. Espinosa, M. Becker, I. Maric, R. Wong, G. Shaw, B. Gaudilliere, N. Aghaeepour, D. Stevenson, I. Stelzer, L. Peterson and 7 other author(s). Trends in Molecular Medicine, (2021)
3 years ago by @becker
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project:bmbf
myown
biology
modeling
cofirst
approach
complications
pregnancy
data
holistic
medicine
p21
systems
driven
project:bmbfmyownbiologymodelingcofirstapproachcomplicationspregnancydataholisticmedicinep21systemsdriven
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1Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset
I. Stelzer, M. Ghaemi, X. Han, K. Ando, J. Hedou, D. Feyaerts, L. Peterson, K. Rumer, E. Tsai, E. Ganio and 28 other author(s). Science Translational Medicine, (2021)
3 years ago by @becker
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labor
project:bmbf
myown
mining
of
pregnancy
data
paper:cordis
machine
multiomics
learning
onset
laborproject:bmbfmyownminingofpregnancydatapaper:cordismachinemultiomicslearningonset
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1Multitask learning and benchmarking with clinical time series data
H. Harutyunyan, H. Khachatrian, D. Kale, G. Ver Steeg, and A. Galstyan. Scientific data, 6 (1): 1--18 (2019)
3 years ago by @becker
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multitask
task
serial
clinical
paper:milkreview:selected
data
learning
time
multi
citedby:scholar:count:322
citedby:scholar:timestamp:2021-5-29
multitasktaskserialclinicalpaper:milkreview:selecteddatalearningtimemulticitedby:scholar:count:322citedby:scholar:timestamp:2021-5-29
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1Challenges in the integration of omics and non-omics data
E. López de Maturana, L. Alonso, P. Alarcón, I. Mart\'ın-Antoniano, S. Pineda, L. Piorno, M. Calle, and N. Malats. Genes, 10 (3): 238 (2019)
3 years ago by @becker
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citedby:scholar:timestamp:2021-5-28
citedby:scholar:count:31
clinical
paper:milkreview:selected
omics
data
multiomics
paper:milkreview
non
multi
integration
ehr
citedby:scholar:timestamp:2021-5-28citedby:scholar:count:31clinicalpaper:milkreview:selectedomicsdatamultiomicspaper:milkreviewnonmultiintegrationehr
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3Mining electronic health records: towards better research applications and clinical care
P. Jensen, L. Jensen, and S. Brunak. Nature Reviews Genetics, 13 (6): 395--405 (2012)
3 years ago by @becker
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paper
citedby:scholar:timestamp:2021-5-12
fastcor
data
citedby:scholar:count:1334
paper:fastcor
ehr
papercitedby:scholar:timestamp:2021-5-12fastcordatacitedby:scholar:count:1334paper:fastcorehr
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