CLeAR

Blog

Welcome to our blog! Below, you’ll find accessible versions of our research. These blog entries are written by CLeAR members.

BlogEnhancing DNN with Symbolic Knowledge (ICRA 2021)

In two recent papers, we propose to view symbolic knowledge as another form of data. The big question is: how can represent and train models to be consistent with this data? We propose a graph embedding network framework that projects logic formulae (and assignments) onto a manifold via an augmented Graph Neural Network (GNN). These embeddings can then be used in a logic loss that can be used to guide deep models during training. Experiments show that our approach improves the performance of deep models.

BlogDiscriminator Gradient $f$low (ICLR 2021)

In this post, we describe a new technique for improving the quality of samples from deep generative models. We introduce Discriminator Gradient $f$low (DG$f$low), which significantly improves samples from deep generative models using the gradient flow of entropy-regularized $f$-divergences between the generated and real data distributions.

Many everyday tasks require multiple sensory modalities to perform successfully. For example, humans use vision to locate the carton and can infer from a simple grasp how much liquid the carton contains. This inference is performed robustly using a power-efficient neural substrate — compared to current artificial systems, human brains require far less energy. In this work, we gain inspiration from biological systems, which are asynchronous and event-driven. We contribute an event-driven visual-tactile perception system, comprising NeuTouch — a biologically-inspired tactile sensor — and the VT-SNN for multi-modal spike-based perception.