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1Varimax PCA
https://twitter.com/karlrohe/status/1513528399201021965?t=UhJUk71F5_eYfusM7qY-YA&s=19
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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1Introducing Neural Structured Learning in TensorFlow | by TensorFlow | TensorFlow | Medium
https://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd
3 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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1Cohen’s d is biased | basic statistics
https://garstats.wordpress.com/2018/04/04/dbias/
3 years ago by @becker
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3Arxiv Sanity Preserver
http://www.arxiv-sanity.com/
3 years ago by @hotho
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2NLP's Clever Hans Moment has Arrived
https://thegradient.pub/nlps-clever-hans-moment-has-arrived/
4 years ago by @hotho
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1Bridging Relational and Deep Learning
https://people.cs.kuleuven.be/~sebastijan.dumancic/RelationalDeepLearning/index.html
5 years ago by @hotho
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1Data Lake 3.0 Part 4 - Cutting Storage Overhead in Half with HDFS Erasure Coding - Hortonworks
https://de.hortonworks.com/blog/data-lake-3-0-part-4-cutting-storage-overhead-in-half-with-hdfs-erasure-coding/
5 years ago by @becker
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1Using Provenance to Debug Training Data for Software 2.0 · Stanford DAWN
https://dawn.cs.stanford.edu//2018/06/21/debugging/
5 years ago by @hotho
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1Learning to Learn Concept Descriptions
https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf
6 years ago by @hotho
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1Artificial intelligence is about to transform science journalism – here’s how to prepare for it. - Public Understanding of Science Blog
The rise of artificial intelligence has recently led to bots writing real news stories about sports, finance and politics. As yet, bots have not turned their attention to science, but the changes AI bots could unleash in science writing are remarkable.
6 years ago by @hotho
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1Gary King instrumental in developing new model for academic collaboration with industry | Harvard Institute for Quantitative Social Science
IQSS Director Gary King and Nate Persily of Stanford Law School have developed a new model for academic collaboration with industry.
6 years ago by @hotho
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1Activity Inequality
http://activityinequality.stanford.edu/
6 years ago by @hotho
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4Google AI Blog: Advances in Semantic Textual Similarity
https://ai.googleblog.com/2018/05/advances-in-semantic-textual-similarity.html
6 years ago by @thoni
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1The NarrativeQA Reading Comprehension Challenge – Arxiv Vanity
https://www.arxiv-vanity.com/papers/1712.07040v1/
6 years ago by @hotho
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2rasbt/python-machine-learning-book-2nd-edition: The "Python Machine Learning (2nd edition)" book code repository and info resource
python-machine-learning-book-2nd-edition - The "Python Machine Learning (2nd edition)" book code repository and info resource
7 years ago by @hotho
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book
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1Reading List « Deep Learning
taught learni
8 years ago by @hotho
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4Deep Learning for NLP - NAACL 2013 Tutorial
Deep Learning for Natural Language Processing (without Magic)
8 years ago by @hotho
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nlp
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nlptoreaddeeptutoriallearning
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1Paper über Vergleichbarkeit von 15$ PPD42NS mit "professioneller" Hardware · Issue #12 · opendata-stuttgart/meta · GitHub
https://github.com/opendata-stuttgart/meta/issues/12
8 years ago by @hotho
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9Representation 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 months ago by @hotho
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perspectives
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representation
toreadperspectivesreviewlearningrepresentation
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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.
a year ago by @hotho
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toread
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5Training 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.
a year ago by @hotho
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toread
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1Graph representation learning in biomedicine and healthcare
M. Li, K. Huang, and M. Zitnik. Nature Biomedical Engineering, (2022)
a year ago by @becker
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neural
learning
biomedicine
medicine
representation
networks
graph
health
themis
healthcare
toreadneurallearningbiomedicinemedicinerepresentationnetworksgraphhealththemishealthcare
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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)
a year ago by @becker
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fairness
toread
machine
learning
algorithmic
themis
fairnesstoreadmachinelearningalgorithmicthemis
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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)
2 years ago by @becker
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confounder
confounding
score
propensity
blei
toread
large
scale
confounderconfoundingscorepropensitybleitoreadlargescale
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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
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transformer
citedby:scholar:timestamp:2022-6-29
network
graph
citedby:scholar:count:85generalizationgraphstoreadneuraltransformercitedby:scholar:timestamp:2022-6-29networkgraph
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1Building a knowledge graph to enable precision medicine
P. Chandak, K. Huang, and M. Zitnik. bioRxiv, (2022)
2 years ago by @becker
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precision
medicine
knowledge
graph
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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
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citedby:scholar:count:1
normalizing
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layers
flow
programming
modelingdeepneuralprobabilisticlearningnetworkcitedby:scholar:timestamp:2022-5-12toreadcitedby:scholar:count:1normalizingprogramslayersflowprogramming
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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
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reasoning
neural
language
model
knowledge
network
jure
graph
nlptoreadreasoningneurallanguagemodelknowledgenetworkjuregraph
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3MIRACLE: 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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citedby:scholar:count:1
data
imputation
machine
impute
learning
citedby:scholar:timestamp:2022-3-21
missing
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value
toreadcitedby:scholar:count:1dataimputationmachineimputelearningcitedby:scholar:timestamp:2022-3-21missingcausalvalue
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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)
2 years ago by @becker
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cytometry
mass
bayesian
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deep
neural
bayes
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flow
cytof
cytometrymassbayesiantoreaddeepneuralbayeslearningnetworksflowcytof
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3Joint 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)
3 years ago by @becker
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joint
autoencoder
toread
optimization
clustering
embedding
jointautoencodertoreadoptimizationclusteringembedding
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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)
3 years ago by @becker
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drug
citedby:scholar:count:10
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gene
citedby:scholar:timestamp:2021-10-6
project:contraception
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drugcitedby:scholar:count:10toreadgenecitedby:scholar:timestamp:2021-10-6project:contraceptionproteininteractomeinteraction
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3Temporal 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)
3 years ago by @becker
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neal
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disease
deep
clustering
cordis
citedby:scholar:count:6
progression
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learning
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joe
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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)
3 years ago by @becker
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2Multi-Task Learning with Deep Neural Networks: A Survey
M. Crawshaw. arXiv preprint arXiv:2009.09796, (2020)
3 years ago by @becker
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citedby:scholar:count:12
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4Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
L. Wu, S. Li, C. Hsieh, and J. Sharpnack. (2019)cite arxiv:1905.10630Comment: Accepted to 2019 Conference on Neural Information Processing Systems.
4 years ago by @hotho
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overfitting
toread
KG
overfittingtoreadKG
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1Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses
A. Dugourd, C. Kuppe, M. Sciacovelli, E. Gjerga, K. Emdal, D. Bekker-Jensen, J. Kranz, E. Bindels, A. Costa, J. Olsen and 3 other author(s). bioRxiv, (2020)
4 years ago by @becker
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omics
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toreadomicsmultiomicscausalcausalitymulti
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2Dimensionality reduction using the sparse linear model
I. Gkioulekas, and T. Zickler. Advances in neural information processing systems, page 271--279. (2011)
4 years ago by @becker
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linear
toread
citedby:scholar:timestamp:2020-6-27
citedby:scholar:count:35
sparse
model
reduction
lineartoreadcitedby:scholar:timestamp:2020-6-27citedby:scholar:count:35sparsemodelreduction
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