autodifferentiation
|au-to-dif-fer-en-ti-a-tion|
🇺🇸
/ˌɔːtoʊdɪfərenʃiˈeɪʃən/
🇬🇧
/ˌɔːtəʊdɪfərenʃiˈeɪʃən/
automatic computation of derivatives
Etymology
'autodifferentiation' originates from Greek and Latin, specifically the prefix 'auto-' from Greek 'autos' where 'autos' meant 'self', and 'differentiation' from Latin 'differentia' where 'differentia' meant 'difference'.
'autodifferentiation' developed as a compound of 'auto-' + 'differentiation' in modern English; the technique itself became named 'automatic differentiation' in 20th-century computational mathematics and later the shorter coinage 'autodifferentiation' and 'autodiff' arose in programming and machine-learning communities.
Initially the parts meant 'self' and 'difference' (or 'making different'); over time the compound evolved to denote the specific computational process of automatically computing derivatives.
Meanings by Part of Speech
Noun 1
a computational technique (also called automatic differentiation or "autodiff") that evaluates exact derivatives of functions represented by computer programs by systematically applying the chain rule to elementary operations; widely used to compute gradients in optimization and machine learning.
Autodifferentiation allows frameworks to compute gradients efficiently and accurately for training neural networks.
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Last updated: 2025/11/25 03:49