Object detection models perform well at localizing and classifying objects that they are
shown during training. However, due to the difficulty and cost associated with creating and …

We propose a novel approach to achieving invariance for deep neural networks in the form
of inducing amnesia to unwanted factors of data through a new adversarial forgetting …

Speaker recognition is increasingly used in several everyday applications including smart
speakers, customer care centers and other speech-driven analytics. It is crucial to accurately …

Learning invariant representations is a critical first step in a number of machine learning
tasks. A common approach is given by the so-called information bottleneck principle in …

Unlike current state-of-the-art methods based on data augmentation and adversarial
frameworks to solve the fair classification problem, this paper proposes the Information …

Supervised machine learning models often associate irrelevant nuisance factors with the
prediction target, which hurts generalization. We propose a framework for training robust …

Surgical state estimators in robot-assisted surgery (RAS)-especially those trained via
learning techniques-rely heavily on datasets that capture surgeon actions in laboratory or …

Small sample sizes are common in many disciplines, which necessitates pooling roughly
similar datasets across multiple institutions to study weak but relevant associations between …

Deep neural network models for speech recognition have achieved great success recently,
but they can learn incorrect associations between the target and nuisance factors of speech …

Speaker recognition is increasingly used in several everyday applications including smart
speakers, customer care centers and other speech-driven analytics. It is crucial to accurately …