When Gradient Descent Meets Derivative-Free Optimization: A Match Made
in Black-Box Scenario

When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario

17 May 2023

Xiang Li

Papers citing "When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario"

7 / 7 papers shown

Title
Prompting Techniques for Secure Code Generation: A Systematic Investigation Catherine Tony Nicolás E. Díaz Ferreyra Markus Mutas Salem Dhiff Riccardo Scandariato SILM 62 9 0 09 Jul 2024
When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges Wang Chao Jiaxuan Zhao Licheng Jiao Lingling Li Fang Liu Shuyuan Yang 54 13 0 19 Jan 2024
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 236 780 0 14 Oct 2021
Constrained Language Models Yield Few-Shot Semantic Parsers Richard Shin C. H. Lin Sam Thomson Charles C. Chen Subhro Roy Emmanouil Antonios Platanios Adam Pauls Dan Klein J. Eisner Benjamin Van Durme 290 196 0 18 Apr 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 278 3,784 0 18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,898 0 31 Dec 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,927 0 20 Apr 2018