Ashish Bajaj

• Delhi Technological University

Advanced neural text classifiers have shown remarkable ability in the task of classification. The investigation illustrates that text classification models have an inherent vulnerability to adversarial texts, where a few words or characters are altered to create adversarial examples that misleads the machine into making incorrect predictions while...

The increasing adoption of deep learning algorithms for automating downstream natural language processing (NLP) tasks has created a need to enhance their capability to assess linguistic acceptability. The CoLA corpus was created to aid in the development of models that can accurately assess grammatical acceptability and evaluate linguistic proficie...

The vast majority of online media rely heavily on the revenues generated by their readers’ views, and due to the abundance of such outlets, they must compete for reader attention. It is a common practise for publishers to employ attention-grabbing headlines as a means to entice users to visit their websites. These headlines, commonly referred to as...

State-of-the-art deep learning algorithms have demonstrated remarkable proficiency in the task of text classification. Despite the widespread use of deep learning-based language models, there remains much work to be done in order to improve the security of these models. This is particularly concerning for their growing use in sensitive applications...

Computer vision applications like traffic monitoring, security checks, self-driving cars, medical imaging, etc., rely heavily on machine learning models. It raises an essential growing concern regarding the dependability of machine learning algorithms, which cannot be entirely trusted due to their fragile nature. This leads us to a dire need for sy...