Asia
Cross-lingual Language Model Pretraining
Alexis CONNEAU, Guillaume Lample
Neural Information Processing Systems http://nips.cc/
ETNet: Error Transition Network for Arbitrary Style Transfer
Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang
The proposed model improvesoverthe state-of-the-art methods with better semantic structures and more adaptivestyle patterndetails.
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, Qi Wu
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.
Spatio-Spectral Graph Neural Networks Simon Geisler, Arthur Kosmala
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of ℓ -step MPGNNs are that their "receptive field" is typically limited to the ℓ-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashing.
Stochastic Frank-Wolfe for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher
Neural Information Processing Systems http://nips.cc/
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
Neural Information Processing Systems http://nips.cc/
Learning Macroscopic Dynamics from Partial Microscopic Observations Mengyi Chen
However, this can be computationally prohibitive for realistic systems.