Asia
Quantum Algorithms for Non-smooth Non-convex Optimization
This paper considers the problem of finding the (ฮด,วซ)-Goldstein stationary point of the Lipschitz continuous objective, which is a rich funct ion class to cover a large number of important applications. We construct a nove l zeroth-order quantum estimator for the gradient of the smoothed surrogate.
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the P ASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
Neural Information Processing Systems http://nips.cc/
Neural networks grown and self-organized by noise
Guruprasad Raghavan, Matt Thomson
Neural Information Processing Systems http://nips.cc/
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Hao Zheng, Faming Fang, Guixu Zhang
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images fromsub-Nyquist samplingk-space datatoaccelerate MRImaging. Inspired by recent deep learning methods, we propose a Cascaded Dilated Dense Network (CDDN)forMRIreconstruction.
