Statistical Learning
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
Variational autoencoders (VAE)areapowerful andwidely-used classofmodels to learn complex data distributions in an unsupervised fashion.
Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, Song Han
Neural Information Processing Systems http://nips.cc/
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation Y u-Jie Zhang
Existing works of coping with covariate shift mainly focused on the "
k-Means Clustering of Lines for Big Data
Neural Information Processing Systems http://nips.cc/
6084e82a08cb979cf75ae28aed37ecd4-AuthorFeedback.pdf
Q2: The objectivefunction of thek-means line clustering problem is confusing.
A Experiment setups In this section, we provide detailed experimental setups for all the tasks discussed in the main paper
CIFAR100 contains 60,000 images in total from 100 classes.
de3d2bb604cfc43c81edd2a31b257f03-Paper-Conference.pdf
Building upon thedataset distillation framework,wemakeakeyobservationthata sharedcommonrepresentation allows for more efficient and effective distillation.
