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{{Short description|Reinforcement learning technique}} {{Machine learning|Reinforcement learning}} ...

{{Short description|Open-source machine learning system for end-to-end data science lifecycle}} ...)|Java]], [[Python (programming language)|Python]], [[(Descriptive Machine Learning)| DML]], [[C (programming language)|C]] ...

...d=36137032 |arxiv=2106.12627|s2cid=235624289 }}</ref> For example, machine learning models could learn to solve [[ground state]]s of quantum many-body systems ...

In the 1980s, researchers from [[cognitive science]] (e.g., [[Judea Pearl]]), [[computer science]] (e ...ams.<ref name="neolearn">{{cite book|last1=Neapolitan|first1=Richard|title=Learning Bayesian Networks|date=2003|publisher=Prentice Hall|isbn=978-0130125347}}</ ...

...eralize effectively from limited examples. It was conceived by [[Google]] researchers for their prominent [[FaceNet]] algorithm for face detection.<ref>{{cite co Triplet loss is designed to support [[metric learning]]. Namely, to assist training models to learn an [[Embedding space|embeddi ...

...input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probabil Geometric feature learning methods extract distinctive geometric features from images. Geometric featu ...

In [[data mining]] and [[machine learning]], {{mvar|k}} {{mvar|q}}-flats algorithm<ref>{{cite journal | last1 = Bradl ==Relation to other machine learning methods== ...

...in 2017 as the '''Sigmoid-weighted Linear Unit''' (SiL) in [[reinforcement learning]].<ref name="Elfwing-Uchibe-Doya_2017" /><ref name="Ramachandran-Zoph-Le_20 In 2017, after performing analysis on [[ImageNet]] data, researchers from [[Google]] indicated that using this function as an [[activation funct ...

...r at [[University of Surrey]], specialising in [[pattern recognition]] and machine intelligence. ...ath>x</math> belonging to class <math>c_j</math> output from the [[Machine learning|learner]] <math>h_i</math>, then the [[median]] rule generates the combined ...

...onvolutional neural networks]] (CNNs) for [[computer vision]] published by researchers at [[Google AI]] in 2019.<ref name=":0">{{Citation |last1=Tan |first1=Mingx ...approximately <math>2^{\phi_0}</math> times. The [[Hyperparameter (machine learning)|hyperparameters]] <math>\alpha</math>, <math>\beta</math>, and <math>\gamm ...

...including [[approximation algorithm]]s, [[coreset]]s, [[Fairness (machine learning)|algorithmic fairness]], and [[inapproximability]].{{r|appointed}} She hold ...on at the [[University of Cologne]] in 2019 as junior professor of machine learning. She moved to her present position in Düsseldorf in 2021.{{r|appointed}} ...

...int=1902.10565 |class=cs.LG |author=David Wu |title=Accelerating Self-Play Learning in Go |date=27 February 2019}}</ref> who also developed the [[Arimaa]] play ...eet.com/accelerating-self-play-learning-in-go|title=Accelerating Self-Play Learning in Go|type=blog post|author=David Wu|date=28 February 2019|access-date=2 No ...

{{Short description|An area of machine learning}} ...ling novel approaches to model analysis, [[transfer learning]], and [[meta-learning]]. ...

...representations |class=cs.CL |eprint=1802.05365 }}</ref> It was created by researchers at the [[Allen Institute for Artificial Intelligence]],<ref>{{Cite web |tit .... It was an instrumental step in the evolution towards [[Transformer (deep learning architecture)|transformer-based]] language modelling. ...

...ref name=":2">{{Cite book |last1=Zhang |first1=Aston |title=Dive into deep learning |last2=Lipton |first2=Zachary |last3=Li |first3=Mu |last4=Smola |first4=Ale ....html |journal=Proceedings of the 31st International Conference on Machine Learning |publisher=PMLR |pages=584–592}}</ref> ...

...org/papers/volume9/shpitser08a/shpitser08a.pdf |journal=Journal of Machine Learning Research |volume=9 |issue=64 |pages=1941–1979 |issn=1533-7928 |access-date= ...hite11/white11.pdf |journal=Causality in Time Series Challenges in Machine Learning |volume=5}}</ref> These models were initially confined to linear equations ...

{{Short description|Finite-state machine where edges carry weights}} ...''weighted finite-state machine''' is a generalization of a [[finite-state machine]] in which the edges have [[Glossary of graph theory#weight|weights]], for ...

...description|Machine learning that combines deep learning and reinforcement learning}} {{Machine learning}} ...

}}</ref> It was discovered repeatedly in very diverse fields such as machine learning ([[AdaBoost]], [[Winnow (algorithm)|Winnow]], Hedge), [[Mathematical optimi ...], which is similar to Minsky and Papert's earlier [[Perceptron|perceptron learning algorithm]]. Later, he generalized the winnow algorithm to weighted majorit ...

...ltzer|first5=Cynthia|last5=Rudin|date=June 2018|journal=Journal of Machine Learning Research|volume=18|issue=234|pages=1–78|arxiv=1704.01701 |url=https://jmlr. Another general criticism of machine-learning based algorithms is since they are data-dependent if the data are biased, t ...