Collaborative Learning and Adaptive Robots (CLeAR) Lab.
Below, you’ll find our contributions to generative modeling/AI and their applications (typically in robotics). At CLeAR, we have developed new deep generative models that are based on gradient flows and structured temporal models that can capture complex relationships, yet are interpretable and useful for planning and decision-making.
We apply techniques from continual learning to the problem of selective forgetting in deep generative models. Our method, dubbed Selective Amnesia, allows users to remap undesired concepts to user-defined ones.
We construct a decomposed latent state space model for perspective-taking for human robot interaction.
We extend gradient flow methods to a variety of high-quality image synthesis tasks using a novel density ratio learning method.
We propose a deep switching state space model that can capture both state-dependent and time-dependent switching patterns in time series data.
We construct a shared latent space from different sensory modalities via contrastive learning.
We present a new method for training GANs via characteristic functions
We present an approach to generate new items for groups of users based on their interaction history.
