Search Results for author:
Found 4 papers, 0 papers with code
Training Artificial Neural Networks by Coordinate Search Algorithm
Finding the optimal values for weights of ANNs is a large-scale optimization problem.
Compact NSGA-II for Multi-objective Feature Selection
In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features.
Multi-objective Binary Coordinate Search for Feature Selection
For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems.
Soft-prompt Tuning for Large Language Models to Evaluate Bias
In this paper, we explore the use of soft-prompt tuning on sentiment classification task to quantify the biases of large language models (LLMs) such as Open Pre-trained Transformers (OPT) and Galactica language model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
