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Evaluating transformers for lightweight action recognition
In video action recognition, transformers consistently reach state-of-the-art accuracy. However, many models are too heavyweight for …
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Channel-temporal attention for first-person video domain adaptation
Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. …
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APRILE: Exploring the molecular mechanisms of drug side effects with explainable graph neural networks
With the majority of people 65 and over taking two or more medicines (polypharmacy), managing the side effects associated with …
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VideoLightFormer: Lightweight action recognition using transformers
Efficient video action recognition remains a challenging problem. One large model after another takes the place of the state-of-the-art …
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PyKale: Knowledge-aware machine learning from multiple sources in python
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant …
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Hop-hop relation-aware graph neural networks
Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either …
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Unifying homophily and heterophily network transformation via motifs
Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node …
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GripNet: Graph information propagation on supergraph for heterogeneous graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and …
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Improving multi-site autism classification based on site-dependence minimisation and second-order functional connectivity
Autism spectrum disorder (ASD) has no objective diagnosis method despite having a high prevalence. Machine learning has been widely …
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Graph node-feature convolution for representation learning
Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature …
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