7.1k citations · 8.6k across the 13 of their papers we have counts for
13 papers
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han +6
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the un…
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…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
Jiani Zhang, Xingjian Shi, Shenglin Zhao +1
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, e…
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
Xingjian Shi, Dit-Yan Yeung
Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence F…
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie +3
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all at…
Spatiotemporal Modeling for Crowd Counting in Videos
Feng Xiong, Xingjian Shi, Dit-Yan Yeung
Region of Interest (ROI) crowd counting can be formulated as a regression problem of learning a mapping from an image or a video frame to a crowd density map. Recently, convolution…
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Xingjian Shi, Zhihan Gao, Leonard Lausen +4
With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public servic…
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-w…
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to…
Dynamic Key-Value Memory Networks for Knowledge Tracing
Jiani Zhang, Xingjian Shi, Irwin King +1
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One impor…
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang +3
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined t…