Oceania
Learning from Bad Data via Generation
Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao
Neural Information Processing Systems http://nips.cc/
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
Neural Information Processing Systems http://nips.cc/
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm
However, it is insufficient to evaluate the stochastic algorithm not to consider the generalization performance, which is roughly the gap between Eq.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballรฉ, George D. Toderici
Inspired by the success of autoregressive priors in probabilistic generative models,weexamineautoregressive,hierarchical, andcombined priorsasalternatives, weighing their costs and benefits in the context of image compression.
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu
Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates the learning of hidden representations of the encoder and decoder together layer by layer,gradually from lowleveltohigh level.
MetaGAN: An Adversarial Approach to Few-Shot Learning
Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
Neural Information Processing Systems http://nips.cc/
Change-pointDetectionforSparseandDense FunctionalDatainGeneralDimensions
We study the problem of change-point detection and localisation for functional data sequentially observed on a generald-dimensional space, where we allow thefunctional curvestobeeither sparsely ordensely sampled.