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1Polylith
https://polylith.gitbook.io/polylith
14 days ago by @bshanks
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1Nondominium: Building the Resource Sharing Engine for the Commons Economy
https://soushi888.github.io/alternef-digital-garden/blog/nondominium-presentation
20 days ago by @bshanks
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1Americans Who Lived Abroad Are Revealing What The US Gets Wrong (And Right)
https://www.buzzfeed.com/victoriavouloumanos/americans-who-lived-abroad-what-us-gets-wrong-right
20 days ago by @bshanks
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1[2301.01047] A Theory of Human-Like Few-Shot Learning
https://arxiv.org/abs/2301.01047
3 months ago by @bshanks
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1Cognitive architecture - Wikipedia
https://en.wikipedia.org/wiki/Cognitive_architecture
4 months ago by @bshanks
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1Request Confirmation Networks: A Cortically Inspired Approach to Neuro-Symbolic Script Execution
https://dash.harvard.edu/bitstreams/1105c321-c5da-4a9f-bacc-606ac1d9f4c8/download
4 months ago by @bshanks
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1Overview — Evil 1.15.0 documentation
https://evil.readthedocs.io/en/latest/overview.html
5 months ago by @bshanks
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1org-almanac
https://alphapapa.github.io/org-almanac/
5 months ago by @bshanks
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1Why Choose ULIDs Over Traditional UUIDs or IDs for Database Identification? : r/programming
https://www.reddit.com/r/programming/comments/1ckklm9/why_choose_ulids_over_traditional_uuids_or_ids/
5 months ago by @bshanks
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1Why Choose ULIDs Over Traditional UUIDs or IDs for Database Identification?
https://web.archive.org/web/20250218001008/https://sotergreco.com/why-choose-ulids-over-traditional-uuids-or-ids-for-database-identification
5 months ago by @bshanks
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1Org-Mode Reference Card
https://orgmode.org/worg/orgcard.html
5 months ago by @bshanks
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1EasyOrg - Org mode Made Easy - Agenda and Editor
https://easyorgmode.com/
5 months ago by @bshanks
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1Emacs: Xah Fly Keys. Key for Org-Mode
http://xahlee.info/emacs/misc/xah-fly-keys_org_mode.html
5 months ago by @bshanks
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1worf.el documentation
https://oremacs.com/worf/README.html
5 months ago by @bshanks
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1For Emacsers -> Evilers, Why did you stay after making the switch? : r/emacs
https://www.reddit.com/r/emacs/comments/ue9q6l/for_emacsers_evilers_why_did_you_stay_after/
5 months ago by @bshanks
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1GitHub - noctuid/evil-guide: Draft of a guide for using emacs with evil
https://github.com/noctuid/evil-guide
5 months ago by @bshanks
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1Migrating to Spacemacs Layers | Modern Emacs
https://www.modernemacs.com/post/migrate-layers/
5 months ago by @bshanks
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1emacs, evil, evil-search mode, and the cgn command… | by lindsay | Medium
https://medium.com/@lynzt/emacs-evil-evil-search-mode-and-the-cgn-command-839c633ba7f3
5 months ago by @bshanks
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1Dimensions Tour
https://www.dimensions-math.org/Dim_tour_E.htm
5 months ago by @bshanks
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1The Ultimate Guide Prioritization Frameworks in Product Management
https://www.productplan.com/learn/product-management-frameworks/
6 months ago by @bshanks
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8Representation Learning: A Review and New Perspectives
Y. Bengio, A. Courville, and P. Vincent. IEEE Trans. Pattern Anal. Mach. Intell., 35 (8): 1798-1828 (2013)
2 years ago by @hotho
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learning
perspectives
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1Towards Giving Timely Formative Feedback and Hints to Novice Programmers
J. Jeuring, H. Keuning, S. Marwan, D. Bouvier, C. Izu, N. Kiesler, T. Lehtinen, D. Lohr, A. Peterson, and S. Sarsa. Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education, page 95-115. ACM, (December 2022)
2 years ago by @brusilovsky
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feedback
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3Tuning computer vision models with task rewards
A. Pinto, A. Kolesnikov, Y. Shi, L. Beyer, and X. Zhai. (2023)cite arxiv:2302.08242Comment: 11 pages.
3 years ago by @hotho
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6Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray and 10 other author(s). (2022)cite arxiv:2203.02155.
3 years ago by @hotho
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1Graph representation learning in biomedicine and healthcare
M. Li, K. Huang, and M. Zitnik. Nature Biomedical Engineering, (2022)
3 years ago by @becker
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graph
representation
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graphrepresentationlearningneuralnetworkstoreadthemisbiomedicinemedicinehealthhealthcare
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3Algorithmic 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)
3 years ago by @becker
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2Adjusting for indirectly measured confounding using large-scale propensity score
L. Zhang, Y. Wang, M. Schuemie, D. Blei, and G. Hripcsak. Journal of Biomedical Informatics, (2022)
4 years ago by @becker
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2A generalization of transformer networks to graphs
V. Dwivedi, and X. Bresson. arXiv preprint arXiv:2012.09699, (2020)
4 years ago by @becker
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citedby:scholar:count:85
citedby:scholar:timestamp:2022-6-29
generalization
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citedby:scholar:count:85citedby:scholar:timestamp:2022-6-29generalizationgraphgraphsnetworkneuraltoreadtransformer
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1Building a knowledge graph to enable precision medicine
P. Chandak, K. Huang, and M. Zitnik. bioRxiv, (2022)
4 years ago by @becker
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knowledge
medicine
precision
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1Embedded-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)
4 years ago by @becker
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modelingdeepneuralprobabilisticlearningnetworkcitedby:scholar:timestamp:2022-5-12toreadcitedby:scholar:count:1normalizingprogramslayersflowprogramming
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2Identification of disease treatment mechanisms through the multiscale interactome
C. Ruiz, M. Zitnik, and J. Leskovec. Nature communications, 12 (1): 1--15 (2021)
4 years ago by @nalab
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drugcitedby:scholar:count:10toreadcitedby:scholar:timestamp:2021-10-6geneproject:contraceptionfrom:beckerproteininteractomeinteraction
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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)
4 years ago by @becker
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toreadknowledgegraphreasoningneuralnetworklanguagemodelnlpjure
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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)
4 years ago by @becker
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citedby:scholar:count:1
citedby:scholar:timestamp:2022-3-21
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causalcitedby:scholar:count:1citedby:scholar:timestamp:2022-3-21dataimputationimputelearningmachinemissingtoreadvalue
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1Bayesian Structure Learning with Generative Flow Networks
T. Deleu, A. Góis, C. Emezue, M. Rankawat, S. Lacoste-Julien, S. Bauer, and Y. Bengio. arXiv preprint arXiv:2202.13903, (2022)
4 years ago by @becker
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2Joint optimization of an autoencoder for clustering and embedding
A. Boubekki, M. Kampffmeyer, U. Brefeld, and R. Jenssen. Machine Learning, 110 (7): 1901--1937 (Jul 1, 2021)
4 years ago by @becker
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autoencoder
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2Identification of disease treatment mechanisms through the multiscale interactome
C. Ruiz, M. Zitnik, and J. Leskovec. Nature communications, 12 (1): 1--15 (2021)
5 years ago by @becker
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citedby:scholar:timestamp:2021-10-6
drug
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2Temporal phenotyping using deep predictive clustering of disease progression
C. Lee, and M. Van Der Schaar. International Conference on Machine Learning, page 5767--5777. PMLR, (2020)
5 years ago by @becker
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citedby:scholar:count:6
citedby:scholar:timestamp:2021-9-15
clustering
cordis
deep
disease
joe
learning
neal
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1Robo-texts and Mecha-morphs: The Rise of Robotech, the Coming of Comico, and the Transmedial Joys of the 1980s ‘Japanimated’ Explosion
J. Alexandratos, and D. Yezbick. (2021)
5 years ago by @iglesia
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comics
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1Multi-task learning for dense prediction tasks: A Survey
S. Vandenhende, S. Georgoulis, W. Van Gansbeke, M. Proesmans, D. Dai, and L. Van Gool. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2021)
5 years ago by @becker
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citedby:scholar:count:6
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1Multi-Task Learning with Deep Neural Networks: A Survey
M. Crawshaw. arXiv preprint arXiv:2009.09796, (2020)
5 years ago by @becker
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citedby:scholar:count:12
citedby:scholar:timestamp:2021-5-29
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