Глоссариум по искусственному интеллекту: 2500 терминов. Том 2 - Александр Юрьевич Чесалов

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each leaf node to represent a class label359,360,361.

      Declarative programming is a programming paradigm – a style of building the structure and elements of computer programs – that expresses the logic of a computation without describing its control flow362,363.

      Decoder in general, any ML system that converts from a processed, dense, or internal representation to a more raw, sparse, or external representation. Decoders are often a component of a larger model, where they are frequently paired with an encoder. In sequence-to-sequence tasks, a decoder starts with the internal state generated by the encoder to predict the next sequence. Refer to Transformer for the definition of a decoder within the Transformer architecture364.

      Decompression – used to restore data to uncompressed form after compression365.

      Deductive classifier is a type of artificial intelligence inference engine. It takes as input a set of declarations in a frame language about a domain such as medical research or molecular biology366.

      Deductive Reasoning, also known as logical deduction, is a reasoning method that relies on premises to reach a logical conclusion. It works in a top- down manner, in which the final conclusion is obtained by reducing the general rules that hold the entire domain until only the conclusion is left367.

      Deep Blue was a chess supercomputer developed by IBM. It was the first computer chess player that beat the world cham- pion Garry Kasparov, after six-game match in 1997368.

      Deep Learning (DL) is a subfield of machine learning concerned with algorithms that are inspired by the human brain that works in a hierarchical way. Deep Learning models, which are mostly based on the (artificial) neural networks, have been applied to different fields, such as speech recognition, computer vision, and natural language processing369.

      Deep model is a type of neural network containing multiple hidden layers. Contrast with wide model370.

      Deep neural network is a multilayer network containing several (many) hidden layers of neurons between the input and output layers, which allows modeling complex nonlinear relationships. GNNs are now increasingly used to solve such artificial intelligence problems as speech recognition, natural language processing, computer vision, etc., including in robotics371.

      Deep Q-Network (DQN) in Q-learning, is a deep neural network that predicts Q-functions. Critic is a synonym for Deep Q-Network372.

      DeepMind is an artificial intelligence company founded in 2010 and later acquired by Google in 2014. DeepMind developed Alphago program that beat a human professional Go player for the first time373,374.

      Default logic is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions375.

      Degree of maturity is the degree of clarity (clarity) of the definition, management, measurement, control and implementation of a specific technological process376.

      Demographic parity is a fairness metric that is satisfied if the results of a model’s classification are not dependent on a given sensitive attribute377.

      Denoising it is the task of machine vision to remove noise from an image. It is a common supervised learning approach in which noise is artificially added to the dataset and the system removes it on its own378.

      Dense feature is a feature in which most values are non-zero, typically a Tensor of floating-point values. Contrast with sparse feature379.

      Dense layer – synonym for fully connected layer380.

      Depersonalization of personal data – actions, as a result of which it becomes impossible, without the use of additional information, to determine the ownership of personal data by a specific subject of personal data381,382.

      Depth – the number of layers (including any embedding layers) in a neural network that learn weights. For example, a neural network with 5 hidden layers and 1 output layer has a depth of 6383.

      Depthwise separable convolutional neural network (sepCNN) is a convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions. Also known as Xception. A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n ✕ n ✕ 1), and then second, a pointwise convolution, with length and width of 1 (1 ✕ 1 ✕ n). To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions384.

      Description logic is a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy descriptions logics, and each description logic features a different balance between DL expressivity and reasoning complexity by supporting different sets of mathematical constructors385.

      Design Center is an organizational unit (the entire organization or its subdivision) that performs a full range or part of the work on creating products up to the stage of its mass production, and also has the necessary personnel, equipment and technologies for this386.

      Developmental robotics (DevRob) (also epigenetic robotics) is a scientific field which aims at studying the developmental mechanisms, architectures, and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines387.

      Device is a category of hardware that can run a TensorFlow session, including CPUs, GPUs, and TPUs388.

      DevOps (development & operations) is a set of practices,

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<p>359</p>

Decision Tree [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#decision-tree (дата обращения: 09.04.2023)

<p>360</p>

Decision Tree [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Decision_tree (дата обращения: 09.04.2023)

<p>361</p>

Дерево решений [Электронный ресурс] https://loginom.ru URL: https://loginom.ru/blog/decision-tree (дата обращения: 09.04.2023)

<p>362</p>

Declarative programming [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Declarative_programming (дата обращения: 09.04.2023)

<p>363</p>

Декларативное программирование [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Декларативное_программирование (дата обращения: 09.04.2023)

<p>364</p>

Decoder [Электронный ресурс] https://dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/317857 (дата обращения: 18.02.2022)

<p>365</p>

Decompression [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (дата обращения: 07.07.2022)

<p>366</p>

Deductive classifier [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Deductive_classifier (дата обращения: 09.04.2023)

<p>367</p>

Дедукция, стр. 36 Педагогический словарь: учеб. пособие для студ. высш. П24 учеб. заведений/ [В.И.Загвязинский, А.Ф.Закирова, Т. А. Строкова и др.]; под ред. В.И.Загвязинского, А.Ф.Закировой. – М.: Издательский центр «Академия», 2008. – 352 с. (дата обращения: 09.04.2023)

<p>368</p>

Deep Blue [Электронный ресурс] www.ststworld.com URL: https://www.ststworld.com/deep-blue/ (дата обращения 18.01.2022)

<p>369</p>

Deep Learning (DL) [Электронный ресурс] https://www.algotive.ai URL: https://www.algotive.ai/blog/everything-you-need-to-know-about-deep-learning-the-technology-that-mimics-the-human-brain (дата обращения: 28.03.2023)

<p>370</p>

Deep model [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/deep-model (дата обращения: 28.03.2023)

<p>371</p>

Deep neural network [Электронный ресурс] https://machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/ (дата обращения: 08.02.2022)

<p>372</p>

Deep Q-Network (DQN) [Электронный ресурс] https://machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/check-point-deep-learning-models-keras/ (дата обращения: 24.02.2022)

<p>373</p>

DeepMind [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Google_DeepMind (дата обращения: 03.05.2023)

<p>374</p>

Компания DeepMind [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Google_DeepMind (дата обращения: 03.05.2023)

<p>375</p>

Default logic [Электронный ресурс] https://semanticscholar.org URL: https://www.semanticscholar.org/topic/Default-logic/175799 (дата обращения 18.01.2022)

<p>376</p>

Степень зрелости [Электронный ресурс] https://studbooks.net URL: https://studbooks.net/2028981/informatika/ pyatiurovnevaya_model_zrelosti_tehnologicheskogo_protsessa_ razrabotki_programmnogo_obespecheniya (дата обращения: 02.07.2023)

<p>377</p>

Demographic parity [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#demographic-parity (дата обращения: 09.04.2023)

<p>378</p>

Denoising [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#denoising (дата обращения: 10.07.2023)

<p>379</p>

Dense feature [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/dense-feature (дата обращения: 26.06.2023)

<p>380</p>

Dense layer [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#dense-layer (дата обращения: 26.06.2023)

<p>381</p>

Обезличивание персональных данных [Электронный ресурс] https://www.consultant.ru URL: https://www.consultant.ru/document/ cons_doc_LAW_61801/4f41fe599ce341751e4e34dc50a4b676674 c1416/ (дата обращения: 11.05.2023)

<p>382</p>

Depersonalization of personal data [Электронный ресурс] https://e-zso.com URL: https://e-zso.com/en/policy/ (дата обращения: 11.05.2023)

<p>383</p>

Depth [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/depth (дата обращения: 28.03.2023)

<p>384</p>

Depthwise separable convolutional neural network (sepCNN) [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#depthwise-separable-convolutional-neural-network-sepcnn (дата обращения: 28.03.2023)

<p>385</p>

Description logic [Электронный ресурс] https://semanticscholar.org URL: https://www.semanticscholar.org/topic/Description-logic/31118 (дата обращения 28.02.2022)

<p>386</p>

Дизайн-центр [Электронный ресурс] https://kartaslov.ru URL: https://kartaslov.ru/значение-слова/дизайн-центр (дата обращения: 09.04.2023)

<p>387</p>

Developmental robotics [Электронный ресурс] https://en.mimi.hu URL: https://en.mimi.hu/artificial_intelligence/developmental_robotics.html (дата обращения 18.01.2022)

<p>388</p>

Device [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/device (дата обращения 07.07.2023)