Implicit deep learning prediction rules generalize the recursive rules of feedforward neural
networks. Such rules are based on the solution of a fixed-point equation involving a single …

Transfer learning aims to help target learners with a different but related source domain.
Open set recognition extends the settings of transfer learning for identifying whether an …

Invariance (defined in a general sense) has been one of the most effective priors for
representation learning. Direct factorization of parametric models is feasible only for a small …

In this work, we study a deep learning-based initialization approach for unimodular
quadratic programs (UQPs), that are concerned with the maximization of a quadratic form …

Finding representations of data that are useful for the underlying prediction task has been an
important and active pursuit in machine learning. Modern-day deep learning algorithms …

Deep implicit models are very recent developments on deep learning. Traditionally, deep
learning methods rely on explicit forward feeding structures. Super deep structures are …

Deep learning is currently one of the most effective approaches in machine learning with
applications in image processing, computer vision, and natural language processing. The …