A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.
Abstract: In this work, we extend the simplex algorithm of linear programming for finding a local minimum of a concave quadratic function subject to box constraints. In order to test the performance ...
Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse ...
ABSTRACT: The outbreak of COVID-19 in 2019 resulted in numerous infections and deaths. In order to better study the transmission of COVID-19, this article adopts an improved fractional-order SIR model ...
This repository contains a C++ implementation of both the Quine-McCluskey method and Petrick's method for minimizing Boolean functions. The project systematically reduces Boolean expressions into ...
This paper revisits the robust overfitting phenomenon of adversarial training. Observing that models with better robust generalization performance are less certain in predicting adversarially ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results