Toward robust natural language systems - Moon, Han Cheol (Nanyang Technological University, 2023)
The monumental achievements of deep learning (DL) systems seem to guarantee the absolute superiority and robustness of modern DL systems, but they have shown significant vulnerability to samples specifically crafted to misguide them, namely adversarial examples The adversarial examples are seemingly indistinguishable from original inputs, but they are perturbed to cause misbehavior of the systems. This confronts us with challenging questions regarding their analysis and interpretation. In response, various approaches have been proposed, but the mathematical theory underlying deep learning models depicting the brittleness still remains obscure. Nonetheless, applications with deep learning models have become ubiquitous and continued to spread widely in various areas even at this moment. Providing its potential impacts on society, the ignorance towards this new learning paradigm would pose considerable threats to the safety and reliable operation of DL systems.