2,066 research outputs found
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Dyadic prediction methods operate on pairs of objects (dyads), aiming to
infer labels for out-of-sample dyads. We consider the full and almost full cold
start problem in dyadic prediction, a setting that occurs when both objects in
an out-of-sample dyad have not been observed during training, or if one of them
has been observed, but very few times. A popular approach for addressing this
problem is to train a model that makes predictions based on a pairwise feature
representation of the dyads, or, in case of kernel methods, based on a tensor
product pairwise kernel. As an alternative to such a kernel approach, we
introduce a novel two-step learning algorithm that borrows ideas from the
fields of pairwise learning and spectral filtering. We show theoretically that
the two-step method is very closely related to the tensor product kernel
approach, and experimentally that it yields a slightly better predictive
performance. Moreover, unlike existing tensor product kernel methods, the
two-step method allows closed-form solutions for training and parameter
selection via cross-validation estimates both in the full and almost full cold
start settings, making the approach much more efficient and straightforward to
implement
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Some images that are difficult to recognize on their own may become more
clear in the context of a neighborhood of related images with similar
social-network metadata. We build on this intuition to improve multilabel image
annotation. Our model uses image metadata nonparametrically to generate
neighborhoods of related images using Jaccard similarities, then uses a deep
neural network to blend visual information from the image and its neighbors.
Prior work typically models image metadata parametrically, in contrast, our
nonparametric treatment allows our model to perform well even when the
vocabulary of metadata changes between training and testing. We perform
comprehensive experiments on the NUS-WIDE dataset, where we show that our model
outperforms state-of-the-art methods for multilabel image annotation even when
our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
A key assumption in multi-task learning is that at the inference time the
multi-task model only has access to a given data point but not to the data
point's labels from other tasks. This presents an opportunity to extend
multi-task learning to utilize data point's labels from other auxiliary tasks,
and this way improves performance on the new task. Here we introduce a novel
relational multi-task learning setting where we leverage data point labels from
auxiliary tasks to make more accurate predictions on the new task. We develop
MetaLink, where our key innovation is to build a knowledge graph that connects
data points and tasks and thus allows us to leverage labels from auxiliary
tasks. The knowledge graph consists of two types of nodes: (1) data nodes,
where node features are data embeddings computed by the neural network, and (2)
task nodes, with the last layer's weights for each task as node features. The
edges in this knowledge graph capture data-task relationships, and the edge
label captures the label of a data point on a particular task. Under MetaLink,
we reformulate the new task as a link label prediction problem between a data
node and a task node. The MetaLink framework provides flexibility to model
knowledge transfer from auxiliary task labels to the task of interest. We
evaluate MetaLink on 6 benchmark datasets in both biochemical and vision
domains. Experiments demonstrate that MetaLink can successfully utilize the
relations among different tasks, outperforming the state-of-the-art methods
under the proposed relational multi-task learning setting, with up to 27%
improvement in ROC AUC.Comment: ICLR 2022 Spotligh
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