Extracting and learning structures from data
In this work, we study models which explicitly capture and learn structures from data. For the task of supervised and unsupervised textual grounding, we propose a unified framework which links words to image concepts. A parameter between each word and image concept is learned and the learned parameters are easily interpretable. Next, for the task of generative modeling of multi-agent trajectories, we design models which share parameters based on the relationship between agents in the system to achieve permutation equivariance. This representation is particularly suitable in a multi-agent setting where the identity of the agents is unknown. We achieve better performance than conventional fully connected deep nets. Lastly, we present a framework on how to learn equivariance properties from data; this framework is based on learning how to share parameters in a model. We provide analysis on Gaussian vectors in terms on mean squared error criterion and empirically show that our approach can recover shift and permutation equivariances.
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign