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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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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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1Deterministic python
https://twitter.com/kastnerkyle/status/1473361479143460872?t=-q6YbOcOZ2AXJ_EgW0vkJg&s=19
2 years ago by @becker
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1Home - Leaderboards by Allen AI
The AI2 Leaderboard platform hosts public leaderboards for a variety of AI challenge tasks across multiple research domains.
3 years ago by @jaeschke
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4Browse the State-of-the-Art in Machine Learning | Papers With Code
4726 leaderboards • 2203 tasks • 3914 datasets • 46332 papers with code.
3 years ago by @jaeschke
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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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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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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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1CS 11-747: Neural Networks for NLP
http://phontron.com/class/nn4nlp2019/
4 years ago by @hotho
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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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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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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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mlmachinelearningtrackingmonitoringmanagingdatascienceprojectsmodelproductivitymlflowsacredloggerlogging
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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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1vivekratnavel/omniboard: Web-based dashboard for Sacred
https://github.com/vivekratnavel/omniboard
5 years ago by @becker
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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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4LLaMA: Open and Efficient Foundation Language Models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar and 4 other author(s). (2023)
a month ago by @boggog
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deep
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nlp
deeplearningmlnlp
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2Adam: A Method for Stochastic Optimization
D. Kingma, and J. Ba. (2017)
a month ago by @boggog
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deep
learning
ml
deeplearningml
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27Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun. (2015)
a month ago by @boggog
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deep
learning
ml
deeplearningml
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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)
3 months ago by @becker
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background
bki
ien
informed
integration
knowledge
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linear
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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)
3 months ago by @becker
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backgroundbkignngraphinformedintegrationknowledgelearningmachinemlnetworksneural
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1Neuro-symbolic approaches in artificial intelligence
P. Hitzler, A. Eberhart, M. Ebrahimi, M. Sarker, and L. Zhou. National Science Review, 9 (6): nwac035 (March 2022)
6 months ago by @jaeschke
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ai
deep
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neurosymbolic
aideeplearningmachinemlneurosymbolic
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7On Calibration of Modern Neural Networks
C. Guo, G. Pleiss, Y. Sun, and K. Weinberger. roceedings of the 34th International Conference on Machine Learning, 70, page 1321-1330. PMLR, (2017)
6 months ago by @jaeschke
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calibration
learning
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calibrationlearningmachinemlnetworkneural
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2IndustrialEdgeML - End-to-end edge-based computer vision systemfor Industry 5.0
R. Wagner, M. Matuschek, P. Knaack, M. Zwick, and M. Geiß. Procedia Computer Science, (2023)4th International Conference on Industry 4.0 and Smart Manufacturing.
7 months ago by @scch
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ai
edge
industrial
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aiedgeindustriallearningmachinemlneural-networks
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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
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ml
accountability
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citedby:scholar:count:15
citedby:scholar:timestamp:2023-4-10
aiartificialintelligencemachinelearningmlaccountabilityresponsiblecitedby:scholar:count:15citedby:scholar:timestamp:2023-4-10
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2Model Cards for Model Reporting
M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. Raji, and T. Gebru. Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, (January 2019)
a year ago by @jaeschke
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card
description
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carddescriptionlearningmachinemlmodelreport
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4Will 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. (2022)cite arxiv:2211.04325.
a year ago by @jaeschke
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data
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4Decision Transformer: Reinforcement Learning via Sequence Modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch. (2021)cite arxiv:2106.01345Comment: First two authors contributed equally. Last two authors advised equally.
a year ago by @jaeschke
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deeplearning
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deeplearninglearningmachinemlplkreinforcementrltransformer
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3Pretrained Transformers for Text Ranking: BERT and Beyond
J. Lin, R. Nogueira, and A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.
a year ago by @jaeschke
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bert
deeplearning
information
ir
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bertdeeplearninginformationirlearningmachinemlnetworkneuralretrievaltransformer
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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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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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fuelln
interpretable
machine
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citedby:scholar:count:3
citedby:scholar:timestamp:2022-11-30
fuellninterpretablemachinelearningmlsuperviseddistancecollaborationcitedby:scholar:count:3citedby:scholar:timestamp:2022-11-30
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1CCGL: Contrastive Cascade Graph Learning
X. Xu, F. Zhou, K. Zhang, and S. Liu. IEEE Transactions on Knowledge and Data Engineering, (2022)
a year ago by @jaeschke
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cascade
contrastive
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graph
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cascadecontrastivedeeplearninggraphlearningmachinemlnetworkneural
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2Explainable Deep Learning: A Field Guide for the Uninitiated
G. Ras, N. Xie, M. Gerven, and D. Doran. Journal of Artificial Intelligence Research, (January 2022)
2 years ago by @jaeschke
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deeplearning
explainable
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deeplearningexplainablelearningmachinemlsurvey
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2Explanatory Interactive Machine Learning
S. Teso, and K. Kersting. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, ACM, (January 2019)
2 years ago by @jaeschke
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active
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ki
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1Snorkel: rapid training data creation with weak supervision
A. Ratner, S. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré. The VLDB Journal, 29 (2): 709--730 (May 1, 2020)
2 years ago by @jaeschke
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2Trouble at the source
D. Monroe. Communications of the ACM, 64 (12): 17--19 (December 2021)
2 years ago by @jaeschke
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artificial
bias
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aiartificialbiasintelligencelearningmachinemltraining
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