1 code implementation • 8 Sep 2023 • Arian Prabowo, KaiXuan Chen, Hao Xue, Subbu Sethuvenkatraman, Flora D. Salim
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment.
1 code implementation • 10 Jun 2023 • Arian Prabowo, KaiXuan Chen, Hao Xue, Subbu Sethuvenkatraman, Flora D. Salim
One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home.
no code implementations • 9 May 2023 • Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location.
1 code implementation • 9 May 2023 • Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim
During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.
1 code implementation • 20 Feb 2023 • Arian Prabowo, Wei Shao, Hao Xue, Piotr Koniusz, Flora D. Salim
Further analysis also shows that each pair of sensors also has a unique dynamic.
no code implementations • 19 May 2021 • Wei Shao, Arian Prabowo, Sichen Zhao, Piotr Koniusz, Flora D. Salim
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.
no code implementations • 18 Aug 2020 • Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.
no code implementations • 24 Sep 2019 • Arian Prabowo, Piotr Koniusz, Wei Shao, Flora D. Salim
This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.