94 research outputs found
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare,
transportation systems, and space sciences. Modeling spatiotemporal
dependencies in multivariate signals is challenging due to (1) long-range
temporal dependencies and (2) complex spatial correlations between sensors. To
address these challenges, we propose representing multivariate signals as
graphs and introduce GraphS4mer, a general graph neural network (GNN)
architecture that captures both spatial and temporal dependencies in
multivariate signals. Specifically, (1) we leverage Structured State Spaces
model (S4), a state-of-the-art sequence model, to capture long-term temporal
dependencies and (2) we propose a graph structure learning layer in GraphS4mer
to learn dynamically evolving graph structures in the data. We evaluate our
proposed model on three distinct tasks and show that GraphS4mer consistently
improves over existing models, including (1) seizure detection from
electroencephalography signals, outperforming a previous GNN with
self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from
polysomnography signals, a 4.1 points improvement in macro-F1 score compared to
existing sleep staging models; and (3) traffic forecasting, reducing MAE by
8.8% compared to existing GNNs and by 1.4% compared to Transformer-based
models
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Machine learning models that achieve high overall accuracy often make
systematic errors on important subsets (or slices) of data. Identifying
underperforming slices is particularly challenging when working with
high-dimensional inputs (e.g. images, audio), where important slices are often
unlabeled. In order to address this issue, recent studies have proposed
automated slice discovery methods (SDMs), which leverage learned model
representations to mine input data for slices on which a model performs poorly.
To be useful to a practitioner, these methods must identify slices that are
both underperforming and coherent (i.e. united by a human-understandable
concept). However, no quantitative evaluation framework currently exists for
rigorously assessing SDMs with respect to these criteria. Additionally, prior
qualitative evaluations have shown that SDMs often identify slices that are
incoherent. In this work, we address these challenges by first designing a
principled evaluation framework that enables a quantitative comparison of SDMs
across 1,235 slice discovery settings in three input domains (natural images,
medical images, and time-series data). Then, motivated by the recent
development of powerful cross-modal representation learning approaches, we
present Domino, an SDM that leverages cross-modal embeddings and a novel
error-aware mixture model to discover and describe coherent slices. We find
that Domino accurately identifies 36% of the 1,235 slices in our framework - a
12 percentage point improvement over prior methods. Further, Domino is the
first SDM that can provide natural language descriptions of identified slices,
correctly generating the exact name of the slice in 35% of settings.Comment: ICLR 2022 (Oral
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