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1Using Citations to Explore Academic Literature | Inciteful.xyz
Using citations to find the most relevant literature
10 days ago by @hotho
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1Tutorial 9: Deep Autoencoders — UvA DL Notebooks v1.2 documentation
https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial9/AE_CIFAR10.html
a year ago by @becker
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autoencoder
python
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autoencoderpythonneuraltutorialpytorchdtsclubnetworknn
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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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1calico/scBasset: Sequence-based Modeling of single-cell ATAC-seq using Convolutional Neural Networks.
https://github.com/calico/scBasset
2 years 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
2 years ago by @becker
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1Neural network configuration
https://twitter.com/karpathy/status/1528808361558306817?t=DcAepTf_tNH1FP-wEjzWzQ&s=19
2 years ago by @becker
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1scwatts/fastspar: Rapid and scalable correlation estimation for compositional data
https://github.com/scwatts/fastspar
2 years ago by @becker
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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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5DALL·E 2
https://openai.com/dall-e-2/
2 years ago by @becker
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1Mummichog
https://shuzhao-li.github.io/mummichog.org/
2 years ago by @becker
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metabolomics
python
py
pathway
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1Machine Learning: TensorFlow-Benchmarks auf MacBooks mit M1 Pro und M1 Max | heise online
https://www.heise.de/tests/Machine-Learning-TensorFlow-Benchmarks-auf-MacBooks-mit-M1-Pro-und-M1-Max-6328476.html?seite=all
2 years ago by @hotho
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2VOSviewer - Visualizing scientific landscapes
https://www.vosviewer.com/
2 years ago by @becker
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1Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/
3 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
3 years ago by @becker
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medical
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1Explaining and Interpreting LSTMs | SpringerLink
https://link.springer.com/chapter/10.1007/978-3-030-28954-6_11#fromHistory
3 years ago by @schwemmlein
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neural
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1A Visual Guide to Using BERT for the First Time – Jay Alammar – Visualizing machine learning one concept at a time.
https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
3 years ago by @becker
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processing
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1Your Beginner Guide to Roam Research — Keep Productive
https://www.keepproductive.com/blog/roam-research-guide
3 years ago by @becker
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maps
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notionmapsproductivitylinklinksimmermindnetworkroamresearch
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1HipGraph/Force2Vec: Implementation of Force2Vec method for ICDM 2020 paper titled "Force2Vec: Parallel force-directed graph embedding"
https://github.com/HipGraph/Force2Vec
3 years ago by @becker
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core
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coremulticorelayoutembeddngefficientparallelpythonforcenetworkdirectedgraphmulti
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118 Commands to Monitor Network Bandwidth on Linux server – BinaryTides
18 Commands to Monitor Network Bandwidth on Linux server – BinaryTides
4 years ago by @nosebrain
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linux
tool
network
linuxtoolnetwork
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1WangYueFt/dgcnn
https://github.com/WangYueFt/dgcnn
4 years ago by @becker
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code
cnn
github
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neural
dynamic
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dgcnn
codecnngithubconvoluationalneuraldynamicnetworkgraphdgcnn
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3Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale
M. Seufert, K. Dietz, N. Wehner, S. Geißler, J. Schüler, M. Wolz, A. Hotho, P. Casas, T. Hoßfeld, and A. Feldmann. IEEE Transactions on Network and Service Management, (2024)
a month ago by @hotho
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myown
monitoring
machine
2024
learning
network
traffic
myownmonitoringmachine2024learningnetworktraffic
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1In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network
R. Fallahzadeh, N. Bidoki, I. Stelzer, M. Becker, I. Marić, A. Chang, A. Culos, T. Phongpreecha, M. Xenochristou, D. De Francesco and 1 other author(s). Cytometry Part A, (2022)
a year ago by @becker
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high
myown
dimensional
neural
encoder
network
single
cell
highmyowndimensionalneuralencodernetworksinglecell
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3Subgroup generalization and fairness of graph neural networks
J. Ma, J. Deng, and Q. Mei. Advances in Neural Information Processing Systems, (2021)
a year ago by @becker
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fairness
subgroup
subgroups
neural
citedby:scholar:count:22
gnn
neurips
citedby:scholar:timestamp:2022-12-13
network
graph
themis
nn
fairnesssubgroupsubgroupsneuralcitedby:scholar:count:22gnnneuripscitedby:scholar:timestamp:2022-12-13networkgraphthemisnn
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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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comparison
forest
decision
neural
data
tabular
learning
tree
trees
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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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2A gesture system for graph visualization in virtual reality environments
Y. Huang, T. Fujiwara, Y. Lin, W. Lin, and K. Ma. 2017 ieee pacific visualization symposium (pacificvis), page 41--45. IEEE, (2017)
a year ago by @becker
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citedby:scholar:count:40
virtual
visualization
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citedby:scholar:timestamp:2022-11-25
immermind
m:
network
graph
gesture
m:id:immermind:coordinate:x:-515.505973815918
m:id:immermind:coordinate:y:-42.4267578125
m:id:immermind:node
citedby:scholar:count:40virtualvisualizationrealitycitedby:scholar:timestamp:2022-11-25immermindm:networkgraphgesturem:id:immermind:coordinate:x:-515.505973815918m:id:immermind:coordinate:y:-42.4267578125m:id:immermind:node
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2Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. Karniadakis. Journal of Computational physics, (2019)
2 years ago by @becker
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equation
pde
citedby:scholar:count:3855
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdecitedby:scholar:count:3855neuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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2DeepXDE: A deep learning library for solving differential equations
L. Lu, X. Meng, Z. Mao, and G. Karniadakis. SIAM Review, 63 (1): 208--228 (2021)
2 years ago by @becker
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equation
pde
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
citedby:scholar:count:574
nn
equationpdeneuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialcitedby:scholar:count:574nn
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2Three ways to solve partial differential equations with neural networks—A review
J. Blechschmidt, and O. Ernst. GAMM-Mitteilungen, 44 (2): e202100006 (2021)
2 years ago by @becker
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equation
pde
neural
round1
citedby:scholar:count:48
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdeneuralround1citedby:scholar:count:48partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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3Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
S. Cuomo, V. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli. arXiv preprint arXiv:2201.05624, (2022)
2 years ago by @becker
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equation
pde
citedby:scholar:count:45
neural
round1
partial
network
citedby:scholar:timestamp:2022-11-11
differential
nn
equationpdecitedby:scholar:count:45neuralround1partialnetworkcitedby:scholar:timestamp:2022-11-11differentialnn
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6The max-min hill-climbing Bayesian network structure learning algorithm
I. Tsamardinos, L. Brown, and C. Aliferis. Machine learning, 65 (1): 31--78 (2006)
2 years ago by @becker
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bayesian
citedby:scholar:timestamp:2022-9-23
causation
bayes
correlation
citedby:scholar:count:1912
partial
network
paper:fastcor
bayesiancitedby:scholar:timestamp:2022-9-23causationbayescorrelationcitedby:scholar:count:1912partialnetworkpaper:fastcor
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1A tutorial on regularized partial correlation networks.
S. Epskamp, and E. Fried. Psychological methods, 23 (4): 617 (2018)
2 years ago by @becker
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citedby:scholar:timestamp:2022-9-10
correlation
partial
network
networks
citedby:scholar:count:1004
paper:fastcor
citedby:scholar:timestamp:2022-9-10correlationpartialnetworknetworkscitedby:scholar:count:1004paper:fastcor
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1Genetic and environmental perturbations lead to regulatory decoherence
A. Lea, M. Subramaniam, A. Ko, T. Lehtimäki, E. Raitoharju, M. Kähönen, I. Seppälä, N. Mononen, O. Raitakari, M. Ala-Korpela and 1 other author(s). Elife, (2019)
2 years ago by @becker
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genetic
decoherence
coherence
gene
cordis
correlation
regulatory
citedby:scholar:timestamp:2022-7-28
citedby:scholar:count:28
network
networks
differential
geneticdecoherencecoherencegenecordiscorrelationregulatorycitedby:scholar:timestamp:2022-7-28citedby:scholar:count:28networknetworksdifferential
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2A generalization of transformer networks to graphs
V. Dwivedi, and X. Bresson. arXiv preprint arXiv:2012.09699, (2020)
2 years ago by @becker
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citedby:scholar:count:85
generalization
graphs
toread
neural
transformer
citedby:scholar:timestamp:2022-6-29
network
graph
citedby:scholar:count:85generalizationgraphstoreadneuraltransformercitedby:scholar:timestamp:2022-6-29networkgraph
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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 1 other author(s). Ai Magazine, 43 (1): 46--58 (2022)
2 years ago by @becker
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biology
citedby:scholar:timestamp:2022-6-21
ai
learning
biomedical
network
graph
citedby:scholar:count:1
background
machine
human
knowledge
open
biologycitedby:scholar:timestamp:2022-6-21ailearningbiomedicalnetworkgraphcitedby:scholar:count:1backgroundmachinehumanknowledgeopen
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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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deep
background
micribiome
bioinformatics
neutral
learning
knowledge
network
information
themis
nn
deepbackgroundmicribiomebioinformaticsneutrallearningknowledgenetworkinformationthemisnn
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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
deep
neural
probabilistic
learning
network
citedby:scholar:timestamp:2022-5-12
toread
citedby:scholar:count:1
normalizing
programs
layers
flow
programming
modelingdeepneuralprobabilisticlearningnetworkcitedby:scholar:timestamp:2022-5-12toreadcitedby:scholar:count:1normalizingprogramslayersflowprogramming
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2DINGO: differential network analysis in genomics
M. Ha, V. Baladandayuthapani, and K. Do. Bioinformatics, 31 (21): 3413--3420 (2015)
2 years ago by @becker
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citedby:scholar:count:106
m:id:test2:node
citedby:scholar:timestamp:2022-4-27
analysis
m:id:test2:coordinate:y:-49.76913261413574
m:
m:id:test2:coordinate:x:177.40279006958008
network
differential
paper:fastcor
citedby:scholar:count:106m:id:test2:nodecitedby:scholar:timestamp:2022-4-27analysism:id:test2:coordinate:y:-49.76913261413574m:m:id:test2:coordinate:x:177.40279006958008networkdifferentialpaper:fastcor
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2Differential network analysis by simultaneously considering changes in gene interactions and gene expression
J. Tu, L. Ou-Yang, Y. Zhu, H. Yan, H. Qin, and X. Zhang. Bioinformatics, 37 (23): 4414--4423 (2021)
2 years ago by @becker
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citedby:scholar:count:3
citedby:scholar:timestamp:2022-4-27
analysis
network
differential
paper:fastcor
citedby:scholar:count:3citedby:scholar:timestamp:2022-4-27analysisnetworkdifferentialpaper:fastcor
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1BioNetStat: a tool for biological networks differential analysis
V. Jardim, S. Santos, A. Fujita, and M. Buckeridge. Frontiers in genetics, (2019)
2 years ago by @becker
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citedby:scholar:count:13
citedby:scholar:timestamp:2022-4-27
analysis
network
differential
paper:fastcor
citedby:scholar:count:13citedby:scholar:timestamp:2022-4-27analysisnetworkdifferentialpaper:fastcor
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2GreaseLM: Graph REASoning Enhanced Language Models
X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. Manning, and J. Leskovec. International Conference on Learning Representations, (2021)
2 years ago by @becker
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nlp
toread
reasoning
neural
language
model
knowledge
network
jure
graph
nlptoreadreasoningneurallanguagemodelknowledgenetworkjuregraph
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