Country
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou
To achieve the invariance to viewpoints, traditional methods [36, 37] use patch detectors [33, 39] to extract transformation covariant local patches which are then normalized for transformation invariance. Then, invariant descriptors can be extracted on the detected local patches. However, a typical image may have very few pixels for which viewpoint covariant patches can be reliably detected[22].
TANGO: Text-drivenPhotorealisticandRobust3D StylizationviaLightingDecomposition
Theproblem is yet challenging and traditionally requires manual efforts from experts ofprofessional artists and a largeamount oftimecost.
Scaling White-Box Transformers for Vision Jinrui Y ang
The most extensive model described to date is the base model size encompasses 77.6M parameters
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
Bryon Aragam, Arash Amini, Qing Zhou
Itfollows from (2) thatX Np(0, (eB,e )), where (eB,e ): = (I eB) Te (I eB) 1. (3) Wewillassumethat 0, andmoreoverthatrmin( ) rmax( ) 1, i.e. theeigenvaluesof are boundedawayfrom0and1. See (47) inthesupplement ( ;s), which conditionnumber ofsizeO(s).
Debiased Bayesian inference for average treatment effects
Workinginthestandard potential outcomes framework, we propose a data-driven modification to an arbitrary (nonparametric) prior based on the propensity score that corrects for the first-orderposteriorbias,therebyimprovingperformance.Weillustrateourmethod for Gaussian process (GP) priors using (semi-)synthetic data.
