Graph-based multimodal ML model shows improved classification of esophageal motility disorders by fusing HRIM spatio-temporal graphs with patient embeddings.
In: International Conference on Learning Representations (2021)
2 Pith papers cite this work. Polarity classification is still indexing.
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CollideNet achieves state-of-the-art time-to-collision forecasting on three public datasets by combining multi-scale spatial aggregation with temporal disentanglement of trend and seasonality in a hierarchical transformer.
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Multimodal Graph-based Classification of Esophageal Motility Disorders
Graph-based multimodal ML model shows improved classification of esophageal motility disorders by fusing HRIM spatio-temporal graphs with patient embeddings.
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CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting
CollideNet achieves state-of-the-art time-to-collision forecasting on three public datasets by combining multi-scale spatial aggregation with temporal disentanglement of trend and seasonality in a hierarchical transformer.