In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

With the advent of large language models (LLMs), the artificial intelligence revolution in
medicine and radiology is now more tangible than ever. Every day, an increasingly large …

Differentially Private (DP) learning has seen limited success for building large deep learning
models of text, and straightforward attempts at applying Differentially Private Stochastic …

Recently, there has been an increased interest in demographically grounded bias in natural
language processing (NLP) applications. Much of the recent work has focused on describing …

The ability to control for the kinds of information encoded in neural representation has a
variety of use cases, especially in light of the challenge of interpreting these models. We …

One concern with the rise of large language models lies with their potential for significant
harm, particularly from pretraining on biased, obscene, copyrighted, and private information …

An increasing number of works in natural language processing have addressed the effect of
bias on the predicted outcomes, introducing mitigation techniques that act on different parts …

Embeddings are functions that map raw input data to low-dimensional vector
representations, while preserving important semantic information about the inputs. Pre …

Prompt tuning provides an efficient way for users to customize Large Language Models
(LLMs) with their private data in the emerging LLM service scenario. However, the sensitive …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …