Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks.

Graph convolutional networks (GCNs) are widely used in a variety of applications and can be seen as an unstructured version of standard convolutional neural networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these networks, especially in environments with low computational resources. To ease these costs, quantization can be applied to GCNs. However, aggressive quantization of the feature maps can lead to a significant degradation in performance. On a different note, the Haar wavelet transforms are known to be one of the most effective and efficient approaches to compress signals. Therefore, instead of applying aggressive quantization to feature maps, we propose to use Haar wavelet compression and light quantization to reduce the computations involved with the network. We demonstrate that this approach surpasses aggressive feature quantization by a significant margin, for a variety of problems ranging from node classification to point cloud classification and both part and semantic segmentation.

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.

Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem-protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.