276 citations · 664 across the 33 of their papers we have counts for
6 papers · 1 filter
Continual Learning, Fast and Slow
Quang Pham, Chenghao Liu, Steven C. H. Hoi
According to the Complementary Learning Systems (CLS) theory~\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary sys…
Learning Deep Time-index Models for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attracti…
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Mouxiang Chen, Chenghao Liu, Zemin Liu +1
Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH),…
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning
Quang Pham, Chenghao Liu, Steven Hoi
Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of…
Learning Fast and Slow for Online Time Series Forecasting
Quang Pham, Chenghao Liu, Doyen Sahoo +1
The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes…
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Transformers have been actively studied for time-series forecasting in recent years. While often showing promising results in various scenarios, traditional Transformers are not de…