AI for Low-Code for AI

AI for Low-Code for AI

31 May 2023

Vincent J. Hellendoorn

Papers citing "AI for Low-Code for AI"

8 / 8 papers shown

Title
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq Joty Guosheng Lin 282 1,556 0 02 Sep 2021
Evaluating Large Language Models Trained on Code Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Pondé ... Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever Wojciech Zaremba ELM ALM 205 5,454 0 07 Jul 2021
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 702 41,736 0 28 May 2020
CodeBERT: A Pre-Trained Model for Programming and Natural Languages Zhangyin Feng Daya Guo Duyu Tang Nan Duan Xiaocheng Feng ... Linjun Shou Bing Qin Ting Liu Daxin Jiang Ming Zhou 151 2,613 0 19 Feb 2020
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain Hongqiu Wu Tiferet Gazit Miltiadis Allamanis Marc Brockschmidt ELM 123 1,068 0 20 Sep 2019
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI Dakuo Wang Justin D. Weisz Michael J. Muller Parikshit Ram Werner Geyer Casey Dugan Y. Tausczik Horst Samulowitz Alexander G. Gray 200 313 0 05 Sep 2019
Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations Toby Jia-Jun Li Marissa Radensky Justin Jia Kirielle Singarajah Tom Michael Mitchell Brad A. Myers 79 101 0 30 Aug 2019
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 651 130,942 0 12 Jun 2017