【資料:(SIGMOD2015 Tutorial)大規(guī)模時序數(shù)據(jù)挖掘與預(yù)測】《Mining and Forecasting of Big Time-series Data》Yasushi Sakurai, Yasuko Matsubara (Kumamoto U) and Christos Faloutsos (CMU/SCS) 網(wǎng)頁鏈接
【論文:面向高維多變量時序預(yù)測的條件潛層樹模型CLTM】《Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]》F Arabshahi, F Huang, A Anandkumar... (2015) 網(wǎng)頁鏈接 Intro:網(wǎng)頁鏈接
Learning Latent Group Dynamics for Prediction of High Dimensional Time Series
"Are you going to the party?" "Depends, who else is coming?" Predicting the future has always been one of the ambitions of mankind. But how far ?
A Rigorous & Readable Review on RNNs
This post introduces a new Critical Review on Recurrent Neural Networks for Sequence Learning. Twelve nights back, while up late preparing pretty pictures for a review ?
Looking Back at "Finding Structure in Time"
Keeping up with the break-neck pace of research in computer science can be daunting. Even in my comfortable position as a graduate researcher, with no students
Demystifying LSTM Neural Networks
This article provides a basic introduction to Long Short Term Memory Neural Networks. For a more thorough review of RNNs, see the full 33 page review ?
Learning to Read with Recurrent Neural Networks
This is the first in a series of posts on recurrent neural networks. Here I'll provide a small introduction to both the power and problems presented ?
