154 citations · 717 across the 63 of their papers we have counts for
8 papers · 1 filter
Clipped Matrix Completion: A Remedy for Ceiling Effects
Takeshi Teshima, Miao Xu, Issei Sato +1
We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the…
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
Yusuke Tsuzuku, Issei Sato
Data-agnostic quasi-imperceptible perturbations on inputs are known to degrade recognition accuracy of deep convolutional networks severely. This phenomenon is considered to be a p…
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao +3
Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavai…
Bayesian posterior approximation via greedy particle optimization
Futoshi Futami, Zhenghang Cui, Issei Sato +1
In Bayesian inference, the posterior distributions are difficult to obtain analytically for complex models such as neural networks. Variational inference usually uses a parametric…
Variational Inference for Gaussian Process with Panel Count Data
Hongyi Ding, Young Lee, Issei Sato +1
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when ex…
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer
Ryosuke Kamesawa, Issei Sato, Masashi Sugiyama
Learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world. A state-of-the-art method of Gaussian process classific…