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[207] [208] Researchers at Stanford University and the University of California, Berkeley found that, when creating directly executable responses to the latest 50 code generation problems from LeetCode that were rated "easy", the performances of GPT-3.5 and GPT-4 fell from 22% and 52%, respectively, in March 2023, to 2% and 10%, respectively ...
IDS —Intrusion Detection System. IE —Internet Explorer. IEC —International Electrotechnical Commission. IEEE —Institute of Electrical and Electronics Engineers. IETF —Internet Engineering Task Force. IFL —Integrated Facility for Linux. IGMP —Internet Group Management Protocol. IGRP —Interior Gateway Routing Protocol.
Proprietary freeware (some versions were under Apache License 2.0) Google Authenticator is a software-based authenticator by Google. It implements multi-factor authentication services using the time-based one-time password (TOTP; specified in RFC 6238) and HMAC-based one-time password (HOTP; specified in RFC 4226), for authenticating users of ...
The GPT-1 architecture was a twelve-layer decoder-only transformer, using twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). Rather than simple stochastic gradient descent , the Adam optimization algorithm was used; the learning rate was increased linearly from zero over the first 2,000 updates to a ...
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CAPTCHA. This CAPTCHA ( reCAPTCHA v1) of "smwm" obscures its message from computer interpretation by twisting the letters and adding a slight background color gradient. A CAPTCHA ( / ˈkæp.tʃə / KAP-chə) is a type of challenge–response test used in computing to determine whether the user is human in order to deter bot attacks and spam.
Stack Overflow is a question-and-answer website for computer programmers. It is the flagship site of the Stack Exchange Network. [2] [3] [4] It was created in 2008 by Jeff Atwood and Joel Spolsky. [5] [6] It features questions and answers on certain computer programming topics. [7] [8] [9] It was created to be a more open alternative to earlier ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.