104 citations · 167 across the 12 of their papers we have counts for
5 papers · 1 filter
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy +6
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in…
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
Zhihan Gao, Xingjian Shi, Hao Wang +4
Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulation with complex physical models and are hence both expensive in computation and dem…
Robust Probabilistic Time Series Forecasting
TaeHo Yoon, Youngsuk Park, Ernest K. Ryu +1
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could…
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Hao Wang, Yifei Ma, Hao Ding +1
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effe…
Graph-Relational Domain Adaptation
Zihao Xu, Hao He, Guang-He Lee +2
Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; t…