In this work, we study network binarization (i.e., binary neural networks, BNNs), which is one of the most promising techniques in compression for convolutional networks (CNNs). Although prior work has introduced many methods that improve accuracy BNNs by minimizing quantization error, there remains a non-negligible performance gap between binarized model and full-precision model. Given feature...

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply...

The increasing availability of co-crystallized protein-protein complexes provides an opportunity to use template-based modeling for protein-protein docking. Structure alignment techniques are useful in detection of remote target-template similarities. The size of the structure involved in the alignment is important for the success in modeling. This paper describes a systematic large-scale study...