Oceania
Neural Architecture Dilation for Adversarial Robustness Y anxi Li1, Zhaohui Y ang 2,3, Yunhe Wang
However, a recently discovered shortcoming of CNNs is that they are vulnerable to adversarial attacks.
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
Falconn++ is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach
Deep neural networks often suffer from poor generalization caused by complex and non-convex loss landscapes. One of the popular solutions is Sharpness-A ware Minimization (SAM), which smooths the loss landscape via minimizing the maximized change of training loss when adding a perturbation to the weight.
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
The pseudocode for the NeurHap-refine is as follows: Algorithm 1: The Local Refinement Algorithm NeurHap-refine. Two categories of datasets are used in the paper, Polyploid species and Viral Quasispecies . BW A-MEM [Li, 2013] is used to align reads to the reference genome. The detailed command is (take the 15-strain ZIKV as an example): $ ./bwa Vikalo, 2020a,b] to derive the SNP matrix from the above alignment to ensure a fair comparison.
