8 citations · 10 across the 5 of their papers we have counts for
5 papers
Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
Sara Abdali, Jia He, CJ Barberan +1
The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP). While thei…
Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Sara Abdali, Richard Anarfi, CJ Barberan +2
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutioni…
NeuroView-RNN: It's About Time
CJ Barberan, Sina Alemohammad, Naiming Liu +2
Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a p…
NeuroView: Explainable Deep Network Decision Making
CJ Barberan, Randall Balestriero, Richard G. Baraniuk
Deep neural networks (DNs) provide superhuman performance in numerous computer vision tasks, yet it remains unclear exactly which of a DN's units contribute to a particular decisio…
NFT-K: Non-Fungible Tangent Kernels
Sina Alemohammad, Hossein Babaei, CJ Barberan +4
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks…